CBCL~NO2*Age+Covariates Models with Model Checking
Claire Campbell; prior data cleaning by Hedyeh Ahmadi
Version October 20, 2023
R set up
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Note that the data cleaning and exploration for this analysis is in a separate file called written by Hedyeh Ahmadi.
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## distance, manhattan, minkowskiImporting the Clean Data
HEI_Aim2_Long <- read.csv("HEI_Aim2_Long.csv")
HEI_Aim2_Wide <- read.csv("HEI_Aim2_Wide.csv")
HEI_Aim2_Long_1KidPerFamily <- read.csv("HEI_Aim2_Long_1KidPerFamily.csv")
dim(HEI_Aim2_Long)## [1] 30419 56dim(HEI_Aim2_Wide)## [1] 11866 140dim(HEI_Aim2_Long_1KidPerFamily)## [1] 25125 56names(HEI_Aim2_Long)## [1] "X" "subjectid"
## [3] "rel_family_id" "abcd_site"
## [5] "eventname" "rel_relationship"
## [7] "interview_date" "demo_l_p_select_language___1"
## [9] "cbcl_select_language___1" "rel_group_id"
## [11] "rel_ingroup_order" "rel_same_sex"
## [13] "reshist_addr1_pm252016aa" "sex"
## [15] "interview_age" "race_ethnicity"
## [17] "high.educ" "reshist_addr1_adi_perc"
## [19] "reshist_addr1_adi_wsum" "overall.income.b"
## [21] "overall.income.l" "overall.income.alltp"
## [23] "prnt.empl.bl" "prnt.empl.l"
## [25] "prnt.empl.alltp" "neighb_phenx_avg_p"
## [27] "neighb_phenx_sum_p" "reshist_addr1_popdensity"
## [29] "reshist_addr1_proxrd" "married"
## [31] "married.or.livingtogether" "cbcl_scr_syn_internal_r"
## [33] "cbcl_scr_syn_external_r" "cbcl_scr_syn_totprob_r"
## [35] "cbcl_scr_syn_anxdep_r" "cbcl_scr_syn_withdep_r"
## [37] "cbcl_scr_syn_attention_r" "cbcl_scr_syn_rulebreak_r"
## [39] "cbcl_scr_syn_aggressive_r" "cbcl_scr_syn_internal_t"
## [41] "cbcl_scr_syn_external_t" "cbcl_scr_syn_totprob_t"
## [43] "cbcl_scr_syn_anxdep_t" "cbcl_scr_syn_withdep_t"
## [45] "cbcl_scr_syn_attention_t" "cbcl_scr_syn_rulebreak_t"
## [47] "cbcl_scr_syn_aggressive_t" "reshist_addr1_years"
## [49] "income_midp" "demo_comb_income_v2"
## [51] "reshist_addr1_no2_2016_aavg" "reshist_addr1_o3_2016_annavg"
## [53] "reshist_addr1_pm252016aa_bl" "reshist_addr1_no2_2016_aavg_bl"
## [55] "reshist_addr1_o3_2016_annavg_bl" "high.educ_bl"names(HEI_Aim2_Wide)## [1] "X"
## [2] "subjectid"
## [3] "rel_family_id"
## [4] "rel_group_id"
## [5] "rel_same_sex"
## [6] "sex"
## [7] "race_ethnicity"
## [8] "reshist_addr1_pm252016aa.Baseline"
## [9] "abcd_site.Baseline"
## [10] "interview_date.Baseline"
## [11] "rel_relationship.Baseline"
## [12] "rel_ingroup_order.Baseline"
## [13] "high.educ.Baseline"
## [14] "interview_age.Baseline"
## [15] "demo_l_p_select_language___1.Baseline"
## [16] "cbcl_select_language___1.Baseline"
## [17] "reshist_addr1_adi_perc.Baseline"
## [18] "reshist_addr1_adi_wsum.Baseline"
## [19] "overall.income.b.Baseline"
## [20] "overall.income.l.Baseline"
## [21] "overall.income.alltp.Baseline"
## [22] "prnt.empl.bl.Baseline"
## [23] "prnt.empl.l.Baseline"
## [24] "prnt.empl.alltp.Baseline"
## [25] "neighb_phenx_avg_p.Baseline"
## [26] "neighb_phenx_sum_p.Baseline"
## [27] "reshist_addr1_popdensity.Baseline"
## [28] "reshist_addr1_proxrd.Baseline"
## [29] "married.Baseline"
## [30] "married.or.livingtogether.Baseline"
## [31] "cbcl_scr_syn_internal_r.Baseline"
## [32] "cbcl_scr_syn_external_r.Baseline"
## [33] "cbcl_scr_syn_totprob_r.Baseline"
## [34] "cbcl_scr_syn_anxdep_r.Baseline"
## [35] "cbcl_scr_syn_withdep_r.Baseline"
## [36] "cbcl_scr_syn_attention_r.Baseline"
## [37] "cbcl_scr_syn_rulebreak_r.Baseline"
## [38] "cbcl_scr_syn_aggressive_r.Baseline"
## [39] "cbcl_scr_syn_internal_t.Baseline"
## [40] "cbcl_scr_syn_external_t.Baseline"
## [41] "cbcl_scr_syn_totprob_t.Baseline"
## [42] "cbcl_scr_syn_anxdep_t.Baseline"
## [43] "cbcl_scr_syn_withdep_t.Baseline"
## [44] "cbcl_scr_syn_attention_t.Baseline"
## [45] "cbcl_scr_syn_rulebreak_t.Baseline"
## [46] "cbcl_scr_syn_aggressive_t.Baseline"
## [47] "reshist_addr1_years.Baseline"
## [48] "income_midp.Baseline"
## [49] "demo_comb_income_v2.Baseline"
## [50] "reshist_addr1_no2_2016_aavg.Baseline"
## [51] "reshist_addr1_o3_2016_annavg.Baseline"
## [52] "reshist_addr1_pm252016aa.1.year"
## [53] "abcd_site.1.year"
## [54] "interview_date.1.year"
## [55] "rel_relationship.1.year"
## [56] "rel_ingroup_order.1.year"
## [57] "high.educ.1.year"
## [58] "interview_age.1.year"
## [59] "demo_l_p_select_language___1.1.year"
## [60] "cbcl_select_language___1.1.year"
## [61] "reshist_addr1_adi_perc.1.year"
## [62] "reshist_addr1_adi_wsum.1.year"
## [63] "overall.income.b.1.year"
## [64] "overall.income.l.1.year"
## [65] "overall.income.alltp.1.year"
## [66] "prnt.empl.bl.1.year"
## [67] "prnt.empl.l.1.year"
## [68] "prnt.empl.alltp.1.year"
## [69] "neighb_phenx_avg_p.1.year"
## [70] "neighb_phenx_sum_p.1.year"
## [71] "reshist_addr1_popdensity.1.year"
## [72] "reshist_addr1_proxrd.1.year"
## [73] "married.1.year"
## [74] "married.or.livingtogether.1.year"
## [75] "cbcl_scr_syn_internal_r.1.year"
## [76] "cbcl_scr_syn_external_r.1.year"
## [77] "cbcl_scr_syn_totprob_r.1.year"
## [78] "cbcl_scr_syn_anxdep_r.1.year"
## [79] "cbcl_scr_syn_withdep_r.1.year"
## [80] "cbcl_scr_syn_attention_r.1.year"
## [81] "cbcl_scr_syn_rulebreak_r.1.year"
## [82] "cbcl_scr_syn_aggressive_r.1.year"
## [83] "cbcl_scr_syn_internal_t.1.year"
## [84] "cbcl_scr_syn_external_t.1.year"
## [85] "cbcl_scr_syn_totprob_t.1.year"
## [86] "cbcl_scr_syn_anxdep_t.1.year"
## [87] "cbcl_scr_syn_withdep_t.1.year"
## [88] "cbcl_scr_syn_attention_t.1.year"
## [89] "cbcl_scr_syn_rulebreak_t.1.year"
## [90] "cbcl_scr_syn_aggressive_t.1.year"
## [91] "reshist_addr1_years.1.year"
## [92] "income_midp.1.year"
## [93] "demo_comb_income_v2.1.year"
## [94] "reshist_addr1_no2_2016_aavg.1.year"
## [95] "reshist_addr1_o3_2016_annavg.1.year"
## [96] "reshist_addr1_pm252016aa.2.year"
## [97] "abcd_site.2.year"
## [98] "interview_date.2.year"
## [99] "rel_relationship.2.year"
## [100] "rel_ingroup_order.2.year"
## [101] "high.educ.2.year"
## [102] "interview_age.2.year"
## [103] "demo_l_p_select_language___1.2.year"
## [104] "cbcl_select_language___1.2.year"
## [105] "reshist_addr1_adi_perc.2.year"
## [106] "reshist_addr1_adi_wsum.2.year"
## [107] "overall.income.b.2.year"
## [108] "overall.income.l.2.year"
## [109] "overall.income.alltp.2.year"
## [110] "prnt.empl.bl.2.year"
## [111] "prnt.empl.l.2.year"
## [112] "prnt.empl.alltp.2.year"
## [113] "neighb_phenx_avg_p.2.year"
## [114] "neighb_phenx_sum_p.2.year"
## [115] "reshist_addr1_popdensity.2.year"
## [116] "reshist_addr1_proxrd.2.year"
## [117] "married.2.year"
## [118] "married.or.livingtogether.2.year"
## [119] "cbcl_scr_syn_internal_r.2.year"
## [120] "cbcl_scr_syn_external_r.2.year"
## [121] "cbcl_scr_syn_totprob_r.2.year"
## [122] "cbcl_scr_syn_anxdep_r.2.year"
## [123] "cbcl_scr_syn_withdep_r.2.year"
## [124] "cbcl_scr_syn_attention_r.2.year"
## [125] "cbcl_scr_syn_rulebreak_r.2.year"
## [126] "cbcl_scr_syn_aggressive_r.2.year"
## [127] "cbcl_scr_syn_internal_t.2.year"
## [128] "cbcl_scr_syn_external_t.2.year"
## [129] "cbcl_scr_syn_totprob_t.2.year"
## [130] "cbcl_scr_syn_anxdep_t.2.year"
## [131] "cbcl_scr_syn_withdep_t.2.year"
## [132] "cbcl_scr_syn_attention_t.2.year"
## [133] "cbcl_scr_syn_rulebreak_t.2.year"
## [134] "cbcl_scr_syn_aggressive_t.2.year"
## [135] "reshist_addr1_years.2.year"
## [136] "income_midp.2.year"
## [137] "demo_comb_income_v2.2.year"
## [138] "reshist_addr1_no2_2016_aavg.2.year"
## [139] "reshist_addr1_o3_2016_annavg.2.year"
## [140] "rel_relationship.1_year"names(HEI_Aim2_Long_1KidPerFamily)## [1] "X" "subjectid"
## [3] "rel_family_id" "abcd_site"
## [5] "eventname" "rel_relationship"
## [7] "interview_date" "demo_l_p_select_language___1"
## [9] "cbcl_select_language___1" "rel_group_id"
## [11] "rel_ingroup_order" "rel_same_sex"
## [13] "reshist_addr1_pm252016aa" "sex"
## [15] "interview_age" "race_ethnicity"
## [17] "high.educ" "reshist_addr1_adi_perc"
## [19] "reshist_addr1_adi_wsum" "overall.income.b"
## [21] "overall.income.l" "overall.income.alltp"
## [23] "prnt.empl.bl" "prnt.empl.l"
## [25] "prnt.empl.alltp" "neighb_phenx_avg_p"
## [27] "neighb_phenx_sum_p" "reshist_addr1_popdensity"
## [29] "reshist_addr1_proxrd" "married"
## [31] "married.or.livingtogether" "cbcl_scr_syn_internal_r"
## [33] "cbcl_scr_syn_external_r" "cbcl_scr_syn_totprob_r"
## [35] "cbcl_scr_syn_anxdep_r" "cbcl_scr_syn_withdep_r"
## [37] "cbcl_scr_syn_attention_r" "cbcl_scr_syn_rulebreak_r"
## [39] "cbcl_scr_syn_aggressive_r" "cbcl_scr_syn_internal_t"
## [41] "cbcl_scr_syn_external_t" "cbcl_scr_syn_totprob_t"
## [43] "cbcl_scr_syn_anxdep_t" "cbcl_scr_syn_withdep_t"
## [45] "cbcl_scr_syn_attention_t" "cbcl_scr_syn_rulebreak_t"
## [47] "cbcl_scr_syn_aggressive_t" "reshist_addr1_years"
## [49] "income_midp" "demo_comb_income_v2"
## [51] "reshist_addr1_no2_2016_aavg" "reshist_addr1_o3_2016_annavg"
## [53] "reshist_addr1_pm252016aa_bl" "reshist_addr1_no2_2016_aavg_bl"
## [55] "reshist_addr1_o3_2016_annavg_bl" "high.educ_bl"# Note we are keeping all families but choosing one kid per family
length(unique(HEI_Aim2_Long$rel_family_id))## [1] 9844length(unique(HEI_Aim2_Long$subjectid)) # matches number of rows of wide data :)## [1] 11866length(unique(HEI_Aim2_Long_1KidPerFamily$rel_family_id))## [1] 9844length(unique(HEI_Aim2_Long_1KidPerFamily$subjectid))## [1] 9844Copy baseline variables to all timepoints for full df
#rename so can use later
names(HEI_Aim2_Long)[names(HEI_Aim2_Long) == 'prnt.empl.bl'] <- 'prnt.empl.b'
#create dataset for table and comparison
baseline_vars <- subset(HEI_Aim2_Long, HEI_Aim2_Long$eventname=="Baseline", select = c("subjectid", "sex", "race_ethnicity", "high.educ", "neighb_phenx_avg_p", "overall.income.b", "prnt.empl.b"))
#rename variables
names(baseline_vars)[names(baseline_vars) == 'sex'] <- 'sex.bl'
names(baseline_vars)[names(baseline_vars) == 'race_ethnicity'] <- 'race_ethnicity.bl'
names(baseline_vars)[names(baseline_vars) == 'high.educ'] <- 'high.educ.bl'
names(baseline_vars)[names(baseline_vars) == 'neighb_phenx_avg_p'] <- 'neighb_phenx_avg_p.bl'
names(baseline_vars)[names(baseline_vars) == 'overall.income.b'] <- 'overall.income.bl'
names(baseline_vars)[names(baseline_vars) == 'prnt.empl.b'] <- 'prnt.empl.bl'
#add to initial df
HEI_Aim2_Long_2 <- merge(HEI_Aim2_Long, baseline_vars, by="subjectid")Descriptive Table before Cleaning
#factor eventname
HEI_Aim2_Long_2$eventname <- as.factor(HEI_Aim2_Long_2$eventname)
HEI_Aim2_Long_2$eventname <- relevel(HEI_Aim2_Long_2$eventname , ref="Baseline")
#create smaller df
df_prior <- subset(HEI_Aim2_Long_2,select=c("subjectid","abcd_site","eventname","interview_age","reshist_addr1_pm252016aa_bl","prnt.empl.bl","overall.income.bl","sex.bl","race_ethnicity.bl","high.educ.bl","neighb_phenx_avg_p.bl","cbcl_scr_syn_internal_r","cbcl_scr_syn_external_r","cbcl_scr_syn_anxdep_r","cbcl_scr_syn_withdep_r","cbcl_scr_syn_attention_r","cbcl_scr_syn_rulebreak_r","cbcl_scr_syn_aggressive_r","cbcl_scr_syn_totprob_r"))
#create table
des_table_prior <- tableby(eventname ~ ., data = df_prior[ , -which(names(df_prior) %in% c("subjectid"))], total=F)
summary(des_table_prior, title = "Descriptive Statistics by Eventname Before Cleaning")##
##
## Table: Descriptive Statistics by Eventname Before Cleaning
##
## | | Baseline (N=11839) | 1-year (N=11200) | 2-year (N=7334) | p value|
## |:----------------------------------------|:------------------:|:-----------------:|:-----------------:|-------:|
## |**abcd_site** | | | | < 0.001|
## | site01 | 406 (3.4%) | 369 (3.3%) | 210 (2.9%) | |
## | site02 | 558 (4.7%) | 548 (4.9%) | 351 (4.8%) | |
## | site03 | 631 (5.3%) | 563 (5.0%) | 372 (5.1%) | |
## | site04 | 745 (6.3%) | 727 (6.5%) | 534 (7.3%) | |
## | site05 | 378 (3.2%) | 357 (3.2%) | 234 (3.2%) | |
## | site06 | 584 (4.9%) | 568 (5.1%) | 379 (5.2%) | |
## | site07 | 339 (2.9%) | 322 (2.9%) | 116 (1.6%) | |
## | site08 | 350 (3.0%) | 339 (3.0%) | 212 (2.9%) | |
## | site09 | 433 (3.7%) | 393 (3.5%) | 227 (3.1%) | |
## | site10 | 739 (6.2%) | 708 (6.3%) | 493 (6.7%) | |
## | site11 | 450 (3.8%) | 400 (3.6%) | 197 (2.7%) | |
## | site12 | 604 (5.1%) | 550 (4.9%) | 274 (3.7%) | |
## | site13 | 728 (6.1%) | 691 (6.2%) | 443 (6.0%) | |
## | site14 | 606 (5.1%) | 583 (5.2%) | 430 (5.9%) | |
## | site15 | 458 (3.9%) | 426 (3.8%) | 266 (3.6%) | |
## | site16 | 1011 (8.5%) | 979 (8.7%) | 640 (8.7%) | |
## | site17 | 578 (4.9%) | 562 (5.0%) | 374 (5.1%) | |
## | site18 | 384 (3.2%) | 376 (3.4%) | 223 (3.0%) | |
## | site19 | 550 (4.6%) | 521 (4.7%) | 397 (5.4%) | |
## | site20 | 707 (6.0%) | 687 (6.1%) | 528 (7.2%) | |
## | site21 | 600 (5.1%) | 531 (4.7%) | 434 (5.9%) | |
## |**interview_age** | | | | < 0.001|
## | Mean (SD) | 118.967 (7.495) | 131.073 (7.714) | 143.361 (7.747) | |
## | Range | 107.000 - 133.000 | 116.000 - 149.000 | 127.000 - 164.000 | |
## |**reshist_addr1_pm252016aa_bl** | | | | 0.745|
## | N-Miss | 651 | 587 | 224 | |
## | Mean (SD) | 7.663 (1.563) | 7.648 (1.561) | 7.650 (1.535) | |
## | Range | 1.722 - 15.902 | 1.722 - 15.902 | 1.722 - 15.902 | |
## |**prnt.empl.bl** | | | | 0.098|
## | N-Miss | 56 | 47 | 22 | |
## | Employed | 8194 (69.5%) | 7826 (70.2%) | 5214 (71.3%) | |
## | Other | 855 (7.3%) | 791 (7.1%) | 474 (6.5%) | |
## | Stay at Home Parent | 2065 (17.5%) | 1941 (17.4%) | 1262 (17.3%) | |
## | Unemployed | 669 (5.7%) | 595 (5.3%) | 362 (5.0%) | |
## |**overall.income.bl** | | | | 0.001|
## | N-Miss | 2 | 1 | 0 | |
## | [<50k] | 3215 (27.2%) | 2930 (26.2%) | 1833 (25.0%) | |
## | [>=100K] | 4544 (38.4%) | 4419 (39.5%) | 2952 (40.3%) | |
## | [>=50K & <100K] | 3065 (25.9%) | 2937 (26.2%) | 2000 (27.3%) | |
## | [Don't Know or Refuse] | 1013 (8.6%) | 913 (8.2%) | 549 (7.5%) | |
## |**sex.bl** | | | | 0.906|
## | Female | 5658 (47.8%) | 5335 (47.6%) | 3481 (47.5%) | |
## | Male | 6181 (52.2%) | 5865 (52.4%) | 3853 (52.5%) | |
## |**race_ethnicity.bl** | | | | < 0.001|
## | N-Miss | 2 | 2 | 0 | |
## | Asian | 250 (2.1%) | 239 (2.1%) | 158 (2.2%) | |
## | Black | 1777 (15.0%) | 1594 (14.2%) | 874 (11.9%) | |
## | Hispanic | 2405 (20.3%) | 2220 (19.8%) | 1411 (19.2%) | |
## | Other | 1243 (10.5%) | 1171 (10.5%) | 724 (9.9%) | |
## | White | 6162 (52.1%) | 5974 (53.3%) | 4167 (56.8%) | |
## |**high.educ.bl** | | | | < 0.001|
## | N-Miss | 14 | 12 | 10 | |
## | < HS Diploma | 592 (5.0%) | 526 (4.7%) | 306 (4.2%) | |
## | Bachelor | 3006 (25.4%) | 2889 (25.8%) | 2002 (27.3%) | |
## | HS Diploma/GED | 1129 (9.5%) | 1007 (9.0%) | 568 (7.8%) | |
## | Post Graduate Degree | 4025 (34.0%) | 3919 (35.0%) | 2611 (35.6%) | |
## | Some College | 3073 (26.0%) | 2847 (25.4%) | 1837 (25.1%) | |
## |**neighb_phenx_avg_p.bl** | | | | 0.003|
## | N-Miss | 8 | 5 | 3 | |
## | Mean (SD) | 3.890 (0.975) | 3.903 (0.969) | 3.938 (0.942) | |
## | Range | 1.000 - 5.000 | 1.000 - 5.000 | 1.000 - 5.000 | |
## |**cbcl_scr_syn_internal_r** | | | | 0.100|
## | N-Miss | 8 | 18 | 5 | |
## | Mean (SD) | 5.043 (5.522) | 5.108 (5.551) | 4.930 (5.614) | |
## | Range | 0.000 - 51.000 | 0.000 - 48.000 | 0.000 - 50.000 | |
## |**cbcl_scr_syn_external_r** | | | | < 0.001|
## | N-Miss | 8 | 18 | 5 | |
## | Mean (SD) | 4.455 (5.867) | 4.176 (5.656) | 3.918 (5.479) | |
## | Range | 0.000 - 49.000 | 0.000 - 47.000 | 0.000 - 46.000 | |
## |**cbcl_scr_syn_anxdep_r** | | | | < 0.001|
## | N-Miss | 8 | 18 | 5 | |
## | Mean (SD) | 2.516 (3.062) | 2.540 (3.072) | 2.322 (2.971) | |
## | Range | 0.000 - 26.000 | 0.000 - 22.000 | 0.000 - 22.000 | |
## |**cbcl_scr_syn_withdep_r** | | | | < 0.001|
## | N-Miss | 8 | 18 | 5 | |
## | Mean (SD) | 1.034 (1.709) | 1.116 (1.778) | 1.201 (1.901) | |
## | Range | 0.000 - 15.000 | 0.000 - 14.000 | 0.000 - 16.000 | |
## |**cbcl_scr_syn_attention_r** | | | | < 0.001|
## | N-Miss | 8 | 18 | 5 | |
## | Mean (SD) | 2.977 (3.495) | 2.858 (3.431) | 2.692 (3.298) | |
## | Range | 0.000 - 20.000 | 0.000 - 19.000 | 0.000 - 19.000 | |
## |**cbcl_scr_syn_rulebreak_r** | | | | < 0.001|
## | N-Miss | 8 | 18 | 5 | |
## | Mean (SD) | 1.192 (1.861) | 1.120 (1.822) | 1.057 (1.833) | |
## | Range | 0.000 - 20.000 | 0.000 - 20.000 | 0.000 - 23.000 | |
## |**cbcl_scr_syn_aggressive_r** | | | | < 0.001|
## | N-Miss | 8 | 18 | 5 | |
## | Mean (SD) | 3.262 (4.355) | 3.056 (4.185) | 2.861 (3.990) | |
## | Range | 0.000 - 36.000 | 0.000 - 33.000 | 0.000 - 32.000 | |
## |**cbcl_scr_syn_totprob_r** | | | | < 0.001|
## | N-Miss | 8 | 18 | 5 | |
## | Mean (SD) | 18.178 (17.968) | 17.520 (17.567) | 16.388 (17.001) | |
## | Range | 0.000 - 139.000 | 0.000 - 128.000 | 0.000 - 161.000 | |Creating variables for modeling and tables
The following variables are time-invariant, will use baseline covariates since PM2.5 collected at baseline: - reshist_addr1_pm252016aa_bl which is the Baseline PM2.5. - reshist_addr1_no2_2016_aavg_bl which is the Baseline NO2. - sex.bl - race_ethnicity.bl - high.educ.bl - prnt.empl.bl - neighb_phenx_avg_p.bl - overall.income.bl
The following variables are time-varying: - all CBCL outcomes - interview_age
Copy baseline variables to all timepoints for 1KidPerFamily
#rename so can use later
names(HEI_Aim2_Long_1KidPerFamily)[names(HEI_Aim2_Long_1KidPerFamily) == 'prnt.empl.bl'] <- 'prnt.empl.b'
#create dataset for table and comparison
baseline_vars_1KidPerFamily <- subset(HEI_Aim2_Long_1KidPerFamily, HEI_Aim2_Long_1KidPerFamily$eventname=="Baseline", select = c("subjectid", "sex", "race_ethnicity", "high.educ", "neighb_phenx_avg_p", "overall.income.b"))
#rename variables
names(baseline_vars_1KidPerFamily)[names(baseline_vars_1KidPerFamily) == 'sex'] <- 'sex.bl'
names(baseline_vars_1KidPerFamily)[names(baseline_vars_1KidPerFamily) == 'race_ethnicity'] <- 'race_ethnicity.bl'
names(baseline_vars_1KidPerFamily)[names(baseline_vars_1KidPerFamily) == 'high.educ'] <- 'high.educ.bl'
names(baseline_vars_1KidPerFamily)[names(baseline_vars_1KidPerFamily) == 'neighb_phenx_avg_p'] <- 'neighb_phenx_avg_p.bl'
names(baseline_vars_1KidPerFamily)[names(baseline_vars_1KidPerFamily) == 'overall.income.b'] <- 'overall.income.bl'
names(baseline_vars_1KidPerFamily)[names(baseline_vars_1KidPerFamily) == 'prnt.empl.b'] <- 'prnt.empl.bl'
#add to initial df
HEI_Aim2_Long_1KidPerFamily_2 <- merge(HEI_Aim2_Long_1KidPerFamily, baseline_vars, by="subjectid")Clean 1KidPerFamily
## Cleaning
#merge Asian into Other group b/c statistically Asian group is too small
tapply(HEI_Aim2_Long_1KidPerFamily_2$race_ethnicity.bl,
HEI_Aim2_Long_1KidPerFamily_2$eventname,table, useNA = "always")## $`1-year`
##
## Asian Black Hispanic Other White <NA>
## 217 1334 1932 964 4803 1
##
## $`2-year`
##
## Asian Black Hispanic Other White <NA>
## 141 715 1232 597 3326 0
##
## $Baseline
##
## Asian Black Hispanic Other White <NA>
## 228 1496 2101 1029 4963 1HEI_Aim2_Long_1KidPerFamily_2$race_ethnicity.bl <-
ifelse(HEI_Aim2_Long_1KidPerFamily_2$race_ethnicity.bl=="Asian","Other",
HEI_Aim2_Long_1KidPerFamily_2$race_ethnicity.bl)
tapply(HEI_Aim2_Long_1KidPerFamily_2$race_ethnicity.bl,
HEI_Aim2_Long_1KidPerFamily_2$eventname,table, useNA = "always")## $`1-year`
##
## Black Hispanic Other White <NA>
## 1334 1932 1181 4803 1
##
## $`2-year`
##
## Black Hispanic Other White <NA>
## 715 1232 738 3326 0
##
## $Baseline
##
## Black Hispanic Other White <NA>
## 1496 2101 1257 4963 1#reformat variables
HEI_Aim2_Long_1KidPerFamily_2$eventname <-
as.factor(HEI_Aim2_Long_1KidPerFamily_2$eventname)
HEI_Aim2_Long_1KidPerFamily_2$eventname <-
relevel(HEI_Aim2_Long_1KidPerFamily_2$eventname , ref="Baseline")
table(HEI_Aim2_Long_1KidPerFamily_2$eventname, useNA = "always")##
## Baseline 1-year 2-year <NA>
## 9818 9251 6011 0HEI_Aim2_Long_1KidPerFamily_2$cbcl_scr_syn_internal_r <- as.numeric(HEI_Aim2_Long_1KidPerFamily_2$cbcl_scr_syn_internal_r)
HEI_Aim2_Long_1KidPerFamily_2$cbcl_scr_syn_external_r <- as.numeric(HEI_Aim2_Long_1KidPerFamily_2$cbcl_scr_syn_external_r)
HEI_Aim2_Long_1KidPerFamily_2$cbcl_scr_syn_anxdep_r <- as.numeric(HEI_Aim2_Long_1KidPerFamily_2$cbcl_scr_syn_anxdep_r)
HEI_Aim2_Long_1KidPerFamily_2$cbcl_scr_syn_withdep_r <- as.numeric(HEI_Aim2_Long_1KidPerFamily_2$cbcl_scr_syn_withdep_r)
HEI_Aim2_Long_1KidPerFamily_2$cbcl_scr_syn_attention_r <- as.numeric(HEI_Aim2_Long_1KidPerFamily_2$cbcl_scr_syn_attention_r)
HEI_Aim2_Long_1KidPerFamily_2$cbcl_scr_syn_rulebreak_r <- as.numeric(HEI_Aim2_Long_1KidPerFamily_2$cbcl_scr_syn_rulebreak_r)
HEI_Aim2_Long_1KidPerFamily_2$cbcl_scr_syn_aggressive_r <- as.numeric(HEI_Aim2_Long_1KidPerFamily_2$cbcl_scr_syn_aggressive_r)
HEI_Aim2_Long_1KidPerFamily_2$cbcl_scr_syn_totprob_r <- as.numeric(HEI_Aim2_Long_1KidPerFamily_2$cbcl_scr_syn_totprob_r)
HEI_Aim2_Long_1KidPerFamily_2$abcd_site <- as.factor(HEI_Aim2_Long_1KidPerFamily_2$abcd_site)
HEI_Aim2_Long_1KidPerFamily_2$subjectid <- as.factor(HEI_Aim2_Long_1KidPerFamily_2$subjectid)
HEI_Aim2_Long_1KidPerFamily_2$prnt.empl.bl <- factor(HEI_Aim2_Long_1KidPerFamily_2$prnt.empl.bl, levels = c("Employed", "Stay at Home Parent", "Unemployed", "Other"))
HEI_Aim2_Long_1KidPerFamily_2$overall.income.bl <- factor(HEI_Aim2_Long_1KidPerFamily_2$overall.income.bl, levels = c("[>=100K]", "[>=50K & <100K]", "[<50k]", "[Don't Know or Refuse]"))
HEI_Aim2_Long_1KidPerFamily_2$sex.bl <- factor(HEI_Aim2_Long_1KidPerFamily_2$sex.bl, levels = c("Male", "Female"))
HEI_Aim2_Long_1KidPerFamily_2$race_ethnicity.bl <- factor(HEI_Aim2_Long_1KidPerFamily_2$race_ethnicity.bl, levels = c("White", "Hispanic", "Black", "Other"))
HEI_Aim2_Long_1KidPerFamily_2$high.educ.bl <- factor(HEI_Aim2_Long_1KidPerFamily_2$high.educ.bl, levels = c("Post Graduate Degree", "Bachelor", "Some College", "HS Diploma/GED", "< HS Diploma"))Create Final Dataset
#create smaller df
df <- subset(HEI_Aim2_Long_1KidPerFamily_2,select=c("subjectid","abcd_site","eventname","interview_age","reshist_addr1_pm252016aa_bl","reshist_addr1_no2_2016_aavg_bl","prnt.empl.bl","overall.income.bl","sex.bl","race_ethnicity.bl","high.educ.bl","neighb_phenx_avg_p.bl","cbcl_scr_syn_internal_r","cbcl_scr_syn_external_r","cbcl_scr_syn_anxdep_r","cbcl_scr_syn_withdep_r","cbcl_scr_syn_attention_r","cbcl_scr_syn_rulebreak_r","cbcl_scr_syn_aggressive_r","cbcl_scr_syn_totprob_r"))
#complete cases because needed for zinb
df_cc <- df[complete.cases(df),]
#center age at 9years-old (i.e., 108 months)
df_cc$interview_age.c9 <- df_cc$interview_age-108
#change to years
df_cc$interview_age.c9.y <- df_cc$interview_age.c9/12
#center pm2.5 to 5 (recommended by WHO)
df_cc$reshist_addr1_pm252016aa_bl.c5 <- df_cc$reshist_addr1_pm252016aa_bl-5
#center no2 to 5.33 (recommended by WHO)
df_cc$reshist_addr1_no2_2016_aavg_bl.c533 <- df_cc$reshist_addr1_no2_2016_aavg_bl-5.33
#center around mean
neighb_phenx_avg_p.bl.cm <- df_cc$neighb_phenx_avg_p.bl - mean(df_cc$neighb_phenx_avg_p.bl)Internalizing
CBCL + AP Longitudinal Models
Zero-Inflated (ZI) Negative Binomial (NB): glmm.zinb in NBZIMM package.
CBCL only needs one nested random intercept since we eliminated the family nesting by choosing one kid per family.
For the negative binomial portion of the model, we do not nest by subject since the ICC across subjects is very low.
internal_zinb_r <- glmm.zinb(cbcl_scr_syn_internal_r ~ reshist_addr1_no2_2016_aavg_bl.c533*interview_age.c9.y + race_ethnicity.bl + high.educ.bl+ prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5, random = ~1|abcd_site/subjectid,
zi_fixed = ~ reshist_addr1_no2_2016_aavg_bl.c533*interview_age.c9.y + race_ethnicity.bl + high.educ.bl+ prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5, zi_random = ~1|abcd_site, data = df_cc)## Computational iterations: 7
## Computational time: 1.264 minutessummary(internal_zinb_r)## Linear mixed-effects model fit by maximum likelihood
## Data: df_cc
## AIC BIC logLik
## NA NA NA
##
## Random effects:
## Formula: ~1 | abcd_site
## (Intercept)
## StdDev: 0.1085146
##
## Formula: ~1 | subjectid %in% abcd_site
## (Intercept) Residual
## StdDev: 0.8510807 1.107308
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: cbcl_scr_syn_internal_r ~ reshist_addr1_no2_2016_aavg_bl.c533 * interview_age.c9.y + race_ethnicity.bl + high.educ.bl + prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5
## Value Std.Error
## (Intercept) 1.2936895 0.05245352
## reshist_addr1_no2_2016_aavg_bl.c533 -0.0011251 0.00296731
## interview_age.c9.y 0.0404698 0.01122769
## race_ethnicity.blHispanic -0.0243655 0.03135028
## race_ethnicity.blBlack -0.3595029 0.03531507
## race_ethnicity.blOther -0.0510735 0.03170440
## high.educ.blBachelor 0.0189736 0.02650239
## high.educ.blSome College 0.0485201 0.03037983
## high.educ.blHS Diploma/GED -0.1307243 0.04312578
## high.educ.bl< HS Diploma -0.1091826 0.05555388
## prnt.empl.blStay at Home Parent 0.0310290 0.02694847
## prnt.empl.blUnemployed 0.1286331 0.04440603
## prnt.empl.blOther 0.1997791 0.03881988
## neighb_phenx_avg_p.bl.cm -0.1218295 0.01132058
## overall.income.bl[>=50K & <100K] 0.1078712 0.02683220
## overall.income.bl[<50k] 0.1694380 0.03382049
## overall.income.bl[Don't Know or Refuse] 0.0699945 0.04256480
## sex.blFemale 0.0498815 0.01969301
## reshist_addr1_pm252016aa_bl.c5 -0.0081566 0.01002029
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.0028425 0.00077516
## DF t-value p-value
## (Intercept) 14510 24.663539 0.0000
## reshist_addr1_no2_2016_aavg_bl.c533 9307 -0.379177 0.7046
## interview_age.c9.y 14510 3.604461 0.0003
## race_ethnicity.blHispanic 9307 -0.777203 0.4371
## race_ethnicity.blBlack 9307 -10.179871 0.0000
## race_ethnicity.blOther 9307 -1.610928 0.1072
## high.educ.blBachelor 9307 0.715920 0.4741
## high.educ.blSome College 9307 1.597115 0.1103
## high.educ.blHS Diploma/GED 9307 -3.031233 0.0024
## high.educ.bl< HS Diploma 9307 -1.965345 0.0494
## prnt.empl.blStay at Home Parent 9307 1.151419 0.2496
## prnt.empl.blUnemployed 9307 2.896748 0.0038
## prnt.empl.blOther 9307 5.146309 0.0000
## neighb_phenx_avg_p.bl.cm 9307 -10.761771 0.0000
## overall.income.bl[>=50K & <100K] 9307 4.020217 0.0001
## overall.income.bl[<50k] 9307 5.009923 0.0000
## overall.income.bl[Don't Know or Refuse] 9307 1.644423 0.1001
## sex.blFemale 9307 2.532954 0.0113
## reshist_addr1_pm252016aa_bl.c5 9307 -0.814005 0.4157
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 14510 -3.666965 0.0002
## Correlation:
## (Intr) rs_1_2_2016__.533
## reshist_addr1_no2_2016_aavg_bl.c533 -0.606
## interview_age.c9.y -0.374 0.428
## race_ethnicity.blHispanic -0.025 -0.051
## race_ethnicity.blBlack -0.019 -0.075
## race_ethnicity.blOther -0.081 -0.027
## high.educ.blBachelor -0.178 0.010
## high.educ.blSome College -0.122 0.018
## high.educ.blHS Diploma/GED -0.072 0.004
## high.educ.bl< HS Diploma -0.026 -0.019
## prnt.empl.blStay at Home Parent -0.082 0.005
## prnt.empl.blUnemployed -0.025 -0.010
## prnt.empl.blOther -0.037 -0.002
## neighb_phenx_avg_p.bl.cm -0.176 0.089
## overall.income.bl[>=50K & <100K] -0.118 -0.012
## overall.income.bl[<50k] -0.058 -0.019
## overall.income.bl[Don't Know or Refuse] -0.054 -0.009
## sex.blFemale -0.181 0.000
## reshist_addr1_pm252016aa_bl.c5 -0.305 -0.234
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.342 -0.464
## in_.9. rc_t.H rc_t.B
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic -0.001
## race_ethnicity.blBlack 0.001 0.349
## race_ethnicity.blOther 0.000 0.286 0.256
## high.educ.blBachelor -0.005 -0.019 -0.013
## high.educ.blSome College 0.001 -0.111 -0.084
## high.educ.blHS Diploma/GED 0.002 -0.143 -0.142
## high.educ.bl< HS Diploma -0.006 -0.166 -0.071
## prnt.empl.blStay at Home Parent 0.003 0.044 0.091
## prnt.empl.blUnemployed -0.002 0.010 -0.040
## prnt.empl.blOther 0.003 0.040 0.009
## neighb_phenx_avg_p.bl.cm -0.006 0.029 0.137
## overall.income.bl[>=50K & <100K] -0.006 -0.091 -0.059
## overall.income.bl[<50k] -0.005 -0.145 -0.181
## overall.income.bl[Don't Know or Refuse] -0.010 -0.096 -0.124
## sex.blFemale 0.002 -0.007 -0.017
## reshist_addr1_pm252016aa_bl.c5 -0.007 -0.080 -0.028
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.921 0.004 0.004
## rc_t.O hgh..B hg..SC
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor -0.001
## high.educ.blSome College -0.024 0.455
## high.educ.blHS Diploma/GED -0.006 0.330 0.494
## high.educ.bl< HS Diploma -0.009 0.262 0.406
## prnt.empl.blStay at Home Parent 0.018 -0.028 -0.014
## prnt.empl.blUnemployed 0.010 -0.009 -0.009
## prnt.empl.blOther -0.012 -0.013 -0.033
## neighb_phenx_avg_p.bl.cm 0.040 -0.004 0.061
## overall.income.bl[>=50K & <100K] -0.014 -0.174 -0.277
## overall.income.bl[<50k] -0.081 -0.159 -0.417
## overall.income.bl[Don't Know or Refuse] -0.061 -0.100 -0.250
## sex.blFemale -0.018 0.015 0.023
## reshist_addr1_pm252016aa_bl.c5 -0.023 -0.001 -0.016
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.002 0.004 0.000
## h..HSD h..<HD p..aHP
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma 0.372
## prnt.empl.blStay at Home Parent -0.050 -0.094
## prnt.empl.blUnemployed -0.068 -0.097 0.147
## prnt.empl.blOther -0.010 -0.019 0.158
## neighb_phenx_avg_p.bl.cm 0.056 0.051 0.027
## overall.income.bl[>=50K & <100K] -0.171 -0.113 -0.031
## overall.income.bl[<50k] -0.364 -0.307 -0.054
## overall.income.bl[Don't Know or Refuse] -0.237 -0.218 -0.077
## sex.blFemale 0.015 -0.004 -0.005
## reshist_addr1_pm252016aa_bl.c5 -0.008 -0.016 -0.016
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.002 0.007 0.001
## prn..U prn..O n___..
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther 0.131
## neighb_phenx_avg_p.bl.cm 0.021 0.004
## overall.income.bl[>=50K & <100K] -0.015 -0.050 0.081
## overall.income.bl[<50k] -0.100 -0.139 0.150
## overall.income.bl[Don't Know or Refuse] -0.077 -0.098 0.083
## sex.blFemale 0.020 0.020 0.026
## reshist_addr1_pm252016aa_bl.c5 -0.002 0.000 0.058
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.004 -0.002 0.004
## o..[&< o..[<5 o..KoR
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k] 0.505
## overall.income.bl[Don't Know or Refuse] 0.359 0.482
## sex.blFemale -0.006 -0.007 0.008
## reshist_addr1_pm252016aa_bl.c5 -0.018 -0.029 -0.028
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.007 0.006 0.010
## sx.blF r_1_25
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k]
## overall.income.bl[Don't Know or Refuse]
## sex.blFemale
## reshist_addr1_pm252016aa_bl.c5 -0.004
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.000 0.007
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -3.0443736 -0.7881532 -0.1966888 0.4323385 4.2636811
##
## Number of Observations: 23857
## Number of Groups:
## abcd_site subjectid %in% abcd_site
## 21 9345summary(internal_zinb_r$zi.fit)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## NA NA NA
##
## Random effects:
## Formula: ~1 | abcd_site
## (Intercept) Residual
## StdDev: 0.3843123 0.6077039
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: zp ~ reshist_addr1_no2_2016_aavg_bl.c533 * interview_age.c9.y + race_ethnicity.bl + high.educ.bl + prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5
## Value Std.Error
## (Intercept) -3.932038 0.17295206
## reshist_addr1_no2_2016_aavg_bl.c533 -0.030062 0.01032432
## interview_age.c9.y -0.041964 0.05777560
## race_ethnicity.blHispanic 0.082638 0.07725034
## race_ethnicity.blBlack 0.898765 0.07389087
## race_ethnicity.blOther 0.391823 0.07537015
## high.educ.blBachelor 0.122001 0.06650467
## high.educ.blSome College 0.046538 0.07583991
## high.educ.blHS Diploma/GED 0.595090 0.09099086
## high.educ.bl< HS Diploma 0.819590 0.10610147
## prnt.empl.blStay at Home Parent -0.048504 0.06512104
## prnt.empl.blUnemployed -0.115317 0.09319960
## prnt.empl.blOther 0.018690 0.08708963
## neighb_phenx_avg_p.bl.cm 0.252045 0.02670859
## overall.income.bl[>=50K & <100K] -0.069053 0.06928728
## overall.income.bl[<50k] 0.007307 0.08034764
## overall.income.bl[Don't Know or Refuse] 0.308209 0.08921581
## sex.blFemale -0.076706 0.04597172
## reshist_addr1_pm252016aa_bl.c5 0.084353 0.02432427
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.013474 0.00414233
## DF t-value p-value
## (Intercept) 23817 -22.734846 0.0000
## reshist_addr1_no2_2016_aavg_bl.c533 23817 -2.911796 0.0036
## interview_age.c9.y 23817 -0.726328 0.4676
## race_ethnicity.blHispanic 23817 1.069738 0.2847
## race_ethnicity.blBlack 23817 12.163413 0.0000
## race_ethnicity.blOther 23817 5.198654 0.0000
## high.educ.blBachelor 23817 1.834474 0.0666
## high.educ.blSome College 23817 0.613631 0.5395
## high.educ.blHS Diploma/GED 23817 6.540110 0.0000
## high.educ.bl< HS Diploma 23817 7.724591 0.0000
## prnt.empl.blStay at Home Parent 23817 -0.744831 0.4564
## prnt.empl.blUnemployed 23817 -1.237309 0.2160
## prnt.empl.blOther 23817 0.214601 0.8301
## neighb_phenx_avg_p.bl.cm 23817 9.436857 0.0000
## overall.income.bl[>=50K & <100K] 23817 -0.996625 0.3190
## overall.income.bl[<50k] 23817 0.090942 0.9275
## overall.income.bl[Don't Know or Refuse] 23817 3.454643 0.0006
## sex.blFemale 23817 -1.668558 0.0952
## reshist_addr1_pm252016aa_bl.c5 23817 3.467839 0.0005
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 23817 3.252706 0.0011
## Correlation:
## (Intr) rs_1_2_2016__.533
## reshist_addr1_no2_2016_aavg_bl.c533 -0.675
## interview_age.c9.y -0.611 0.691
## race_ethnicity.blHispanic -0.028 -0.045
## race_ethnicity.blBlack -0.040 -0.066
## race_ethnicity.blOther -0.091 -0.023
## high.educ.blBachelor -0.140 -0.005
## high.educ.blSome College -0.093 0.001
## high.educ.blHS Diploma/GED -0.073 -0.008
## high.educ.bl< HS Diploma -0.019 -0.041
## prnt.empl.blStay at Home Parent -0.061 -0.005
## prnt.empl.blUnemployed -0.017 -0.014
## prnt.empl.blOther -0.026 -0.004
## neighb_phenx_avg_p.bl.cm -0.141 0.061
## overall.income.bl[>=50K & <100K] -0.076 -0.024
## overall.income.bl[<50k] -0.040 -0.028
## overall.income.bl[Don't Know or Refuse] -0.036 -0.022
## sex.blFemale -0.125 0.003
## reshist_addr1_pm252016aa_bl.c5 -0.237 -0.164
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.564 -0.763
## in_.9. rc_t.H rc_t.B
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic 0.001
## race_ethnicity.blBlack 0.011 0.466
## race_ethnicity.blOther 0.006 0.354 0.343
## high.educ.blBachelor -0.011 -0.014 -0.006
## high.educ.blSome College -0.001 -0.114 -0.103
## high.educ.blHS Diploma/GED 0.010 -0.159 -0.160
## high.educ.bl< HS Diploma -0.018 -0.186 -0.102
## prnt.empl.blStay at Home Parent -0.005 0.046 0.107
## prnt.empl.blUnemployed -0.005 0.017 -0.020
## prnt.empl.blOther 0.007 0.057 0.012
## neighb_phenx_avg_p.bl.cm -0.002 0.017 0.130
## overall.income.bl[>=50K & <100K] -0.015 -0.106 -0.091
## overall.income.bl[<50k] -0.011 -0.150 -0.207
## overall.income.bl[Don't Know or Refuse] -0.018 -0.118 -0.153
## sex.blFemale 0.006 -0.003 -0.025
## reshist_addr1_pm252016aa_bl.c5 -0.010 -0.074 -0.028
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.919 0.004 0.001
## rc_t.O hgh..B hg..SC
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor 0.005
## high.educ.blSome College -0.010 0.478
## high.educ.blHS Diploma/GED 0.001 0.409 0.588
## high.educ.bl< HS Diploma -0.002 0.358 0.525
## prnt.empl.blStay at Home Parent 0.024 -0.023 -0.012
## prnt.empl.blUnemployed 0.013 -0.018 -0.005
## prnt.empl.blOther -0.008 -0.017 -0.032
## neighb_phenx_avg_p.bl.cm 0.032 0.003 0.049
## overall.income.bl[>=50K & <100K] -0.024 -0.166 -0.283
## overall.income.bl[<50k] -0.079 -0.159 -0.408
## overall.income.bl[Don't Know or Refuse] -0.069 -0.118 -0.296
## sex.blFemale -0.017 0.006 0.009
## reshist_addr1_pm252016aa_bl.c5 -0.008 0.009 -0.010
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.004 0.009 0.005
## h..HSD h..<HD p..aHP
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma 0.543
## prnt.empl.blStay at Home Parent -0.057 -0.125
## prnt.empl.blUnemployed -0.082 -0.121 0.177
## prnt.empl.blOther -0.021 -0.036 0.166
## neighb_phenx_avg_p.bl.cm 0.050 0.055 0.034
## overall.income.bl[>=50K & <100K] -0.207 -0.155 -0.024
## overall.income.bl[<50k] -0.412 -0.386 -0.051
## overall.income.bl[Don't Know or Refuse] -0.309 -0.285 -0.094
## sex.blFemale 0.010 -0.014 0.011
## reshist_addr1_pm252016aa_bl.c5 0.008 -0.008 -0.010
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.001 0.025 0.014
## prn..U prn..O n___..
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther 0.149
## neighb_phenx_avg_p.bl.cm 0.020 -0.009
## overall.income.bl[>=50K & <100K] -0.014 -0.048 0.061
## overall.income.bl[<50k] -0.080 -0.126 0.123
## overall.income.bl[Don't Know or Refuse] -0.081 -0.109 0.071
## sex.blFemale 0.036 0.018 0.019
## reshist_addr1_pm252016aa_bl.c5 0.004 0.001 0.049
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.009 -0.003 0.002
## o..[&< o..[<5 o..KoR
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k] 0.536
## overall.income.bl[Don't Know or Refuse] 0.440 0.606
## sex.blFemale -0.005 -0.003 -0.005
## reshist_addr1_pm252016aa_bl.c5 -0.016 -0.022 -0.037
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.015 0.011 0.023
## sx.blF r_1_25
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k]
## overall.income.bl[Don't Know or Refuse]
## sex.blFemale
## reshist_addr1_pm252016aa_bl.c5 -0.006
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.000 0.008
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -0.8497488 -0.3085840 -0.2374125 -0.1761323 18.2906536
##
## Number of Observations: 23857
## Number of Groups: 21anova(internal_zinb_r)## numDF denDF F-value
## (Intercept) 1 14510 2665.2972
## reshist_addr1_no2_2016_aavg_bl.c533 1 9307 2.5326
## interview_age.c9.y 1 14510 0.2877
## race_ethnicity.bl 3 9307 18.9623
## high.educ.bl 4 9307 12.1011
## prnt.empl.bl 3 9307 15.3917
## neighb_phenx_avg_p.bl.cm 1 9307 136.6297
## overall.income.bl 3 9307 9.8988
## sex.bl 1 9307 6.4078
## reshist_addr1_pm252016aa_bl.c5 1 9307 0.6223
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 1 14510 13.4466
## p-value
## (Intercept) <.0001
## reshist_addr1_no2_2016_aavg_bl.c533 0.1116
## interview_age.c9.y 0.5917
## race_ethnicity.bl <.0001
## high.educ.bl <.0001
## prnt.empl.bl <.0001
## neighb_phenx_avg_p.bl.cm <.0001
## overall.income.bl <.0001
## sex.bl 0.0114
## reshist_addr1_pm252016aa_bl.c5 0.4302
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.0002VarCorr(internal_zinb_r)## Variance StdDev
## abcd_site = pdLogChol(1)
## (Intercept) 0.01177542 0.1085146
## subjectid = pdLogChol(1)
## (Intercept) 0.72433830 0.8510807
## Residual 1.22613008 1.1073076Assumption checking for ZINB Models
Zero inflated negative binomial (zinb) regression already has overdispersion and excess zeros and this is accounted for in the zinb modeling chosen, “The data distribution combines the negative binomial distribution and the logit distribution”
Details on zinb can be found here: link
For Model Checking we will follow the following pdf: link This info is further detailed/published in books by Cameron and Trivedi (2013) and Hilbe (2014) and in Garay, Hashimoto, Ortega, and Lachos (2011).
They suggest using Pearson residuals.
#Check outlier/residuals with this df
internal_res <- df_cc
internal_res$level1_resid.raw <- residuals(internal_zinb_r)
internal_res$level1_resid.pearson <- residuals(internal_zinb_r, type="pearson")
#Add predicted values (Yhat)
internal_res$cbcl_scr_syn_internal_r_predicted <- predict(internal_zinb_r,internal_res,type="response")
#Incidence
internal_res$incidence <- estimate.probability(internal_res$cbcl_scr_syn_internal_r, method="empirical")
#Plotting histogram of residuals, but may be skewed since using ZINB, so make sure to check below plots
hist(internal_res$level1_resid.pearson)
Incidence vs. X’s Plots
“These plots show each of the independent variables plotted against the incidence as measured by Y (CBCL Outcome). They should be scanned for outliers and curvilinear patterns.”
#age
ggplot(internal_res,aes(incidence,interview_age)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
#pm2.5
ggplot(internal_res,aes(incidence,reshist_addr1_no2_2016_aavg_bl)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
Residuals vs Y (CBCL Outcome) Plot
“This plot shows the residuals versus the dependent variable. It can be used to spot outliers.”
plot(internal_res$level1_resid.pearson, internal_res$cbcl_scr_syn_internal_r)
Residuals vs Yhat Plot
“This plot shows the residuals versus the predicted value (Yhat) of the dependent variable. It can show outliers.”
plot(internal_res$level1_resid.pearson, internal_res$cbcl_scr_syn_internal_r_predicted)
Residuals vs Row Plot
“This plot shows the residuals versus the row numbers. It is used to quickly spot rows that have large residuals.”
plot(as.numeric(rownames(internal_res)),internal_res$level1_resid.pearson)
Residuals vs X’s Plots
“These plots show the residuals plotted against the independent variables. They are used to spot outliers. They are also used to find curvilinear patterns that are not represented in the regression model.”
#age
ggplot(internal_res,aes(level1_resid.pearson,interview_age)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
#pm2.5
ggplot(internal_res,aes(level1_resid.pearson,reshist_addr1_no2_2016_aavg_bl)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
For below models, view Internalizing above for notes.
Externalizing
CBCL + AP Longitudinal Models
external_zinb_r <- glmm.zinb(cbcl_scr_syn_external_r ~ reshist_addr1_no2_2016_aavg_bl.c533*interview_age.c9.y + race_ethnicity.bl + high.educ.bl+ prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5, random = ~1|abcd_site/subjectid,
zi_fixed = ~ reshist_addr1_no2_2016_aavg_bl.c533*interview_age.c9.y + race_ethnicity.bl + high.educ.bl+ prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5, zi_random = ~1|abcd_site, data = df_cc)## Computational iterations: 9
## Computational time: 1.468 minutessummary(external_zinb_r)## Linear mixed-effects model fit by maximum likelihood
## Data: df_cc
## AIC BIC logLik
## NA NA NA
##
## Random effects:
## Formula: ~1 | abcd_site
## (Intercept)
## StdDev: 0.1293724
##
## Formula: ~1 | subjectid %in% abcd_site
## (Intercept) Residual
## StdDev: 1.099444 1.058006
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: cbcl_scr_syn_external_r ~ reshist_addr1_no2_2016_aavg_bl.c533 * interview_age.c9.y + race_ethnicity.bl + high.educ.bl + prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5
## Value Std.Error
## (Intercept) 0.9912141 0.06451899
## reshist_addr1_no2_2016_aavg_bl.c533 -0.0007144 0.00364134
## interview_age.c9.y -0.0063140 0.01227895
## race_ethnicity.blHispanic -0.0551928 0.04004591
## race_ethnicity.blBlack -0.1040765 0.04439928
## race_ethnicity.blOther -0.0377392 0.04074621
## high.educ.blBachelor 0.1074229 0.03414770
## high.educ.blSome College 0.1942691 0.03891034
## high.educ.blHS Diploma/GED 0.0682056 0.05456822
## high.educ.bl< HS Diploma 0.1018384 0.07028178
## prnt.empl.blStay at Home Parent 0.0120721 0.03457345
## prnt.empl.blUnemployed 0.1993709 0.05599139
## prnt.empl.blOther 0.1976230 0.04940597
## neighb_phenx_avg_p.bl.cm -0.1292312 0.01441705
## overall.income.bl[>=50K & <100K] 0.1273511 0.03451008
## overall.income.bl[<50k] 0.2644654 0.04315648
## overall.income.bl[Don't Know or Refuse] 0.1656061 0.05412205
## sex.blFemale -0.2976044 0.02524486
## reshist_addr1_pm252016aa_bl.c5 -0.0169361 0.01264763
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.0024448 0.00085211
## DF t-value p-value
## (Intercept) 14510 15.363138 0.0000
## reshist_addr1_no2_2016_aavg_bl.c533 9307 -0.196187 0.8445
## interview_age.c9.y 14510 -0.514209 0.6071
## race_ethnicity.blHispanic 9307 -1.378238 0.1682
## race_ethnicity.blBlack 9307 -2.344104 0.0191
## race_ethnicity.blOther 9307 -0.926202 0.3544
## high.educ.blBachelor 9307 3.145830 0.0017
## high.educ.blSome College 9307 4.992738 0.0000
## high.educ.blHS Diploma/GED 9307 1.249915 0.2114
## high.educ.bl< HS Diploma 9307 1.449002 0.1474
## prnt.empl.blStay at Home Parent 9307 0.349173 0.7270
## prnt.empl.blUnemployed 9307 3.560743 0.0004
## prnt.empl.blOther 9307 3.999982 0.0001
## neighb_phenx_avg_p.bl.cm 9307 -8.963780 0.0000
## overall.income.bl[>=50K & <100K] 9307 3.690257 0.0002
## overall.income.bl[<50k] 9307 6.128058 0.0000
## overall.income.bl[Don't Know or Refuse] 9307 3.059865 0.0022
## sex.blFemale 9307 -11.788713 0.0000
## reshist_addr1_pm252016aa_bl.c5 9307 -1.339070 0.1806
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 14510 -2.869061 0.0041
## Correlation:
## (Intr) rs_1_2_2016__.533
## reshist_addr1_no2_2016_aavg_bl.c533 -0.599
## interview_age.c9.y -0.326 0.377
## race_ethnicity.blHispanic -0.023 -0.055
## race_ethnicity.blBlack -0.020 -0.077
## race_ethnicity.blOther -0.084 -0.029
## high.educ.blBachelor -0.189 0.012
## high.educ.blSome College -0.130 0.019
## high.educ.blHS Diploma/GED -0.078 0.006
## high.educ.bl< HS Diploma -0.030 -0.017
## prnt.empl.blStay at Home Parent -0.084 0.004
## prnt.empl.blUnemployed -0.025 -0.012
## prnt.empl.blOther -0.037 -0.002
## neighb_phenx_avg_p.bl.cm -0.182 0.089
## overall.income.bl[>=50K & <100K] -0.127 -0.011
## overall.income.bl[<50k] -0.064 -0.017
## overall.income.bl[Don't Know or Refuse] -0.061 -0.006
## sex.blFemale -0.182 0.000
## reshist_addr1_pm252016aa_bl.c5 -0.316 -0.234
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.299 -0.407
## in_.9. rc_t.H rc_t.B
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic 0.000
## race_ethnicity.blBlack 0.001 0.357
## race_ethnicity.blOther 0.000 0.288 0.263
## high.educ.blBachelor -0.004 -0.022 -0.016
## high.educ.blSome College 0.000 -0.111 -0.085
## high.educ.blHS Diploma/GED 0.002 -0.144 -0.147
## high.educ.bl< HS Diploma -0.005 -0.168 -0.077
## prnt.empl.blStay at Home Parent 0.003 0.043 0.092
## prnt.empl.blUnemployed -0.002 0.010 -0.040
## prnt.empl.blOther 0.002 0.041 0.011
## neighb_phenx_avg_p.bl.cm -0.004 0.030 0.136
## overall.income.bl[>=50K & <100K] -0.006 -0.088 -0.061
## overall.income.bl[<50k] -0.004 -0.143 -0.182
## overall.income.bl[Don't Know or Refuse] -0.008 -0.096 -0.125
## sex.blFemale 0.002 -0.008 -0.019
## reshist_addr1_pm252016aa_bl.c5 -0.005 -0.084 -0.033
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.923 0.003 0.003
## rc_t.O hgh..B hg..SC
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor -0.002
## high.educ.blSome College -0.025 0.461
## high.educ.blHS Diploma/GED -0.009 0.339 0.503
## high.educ.bl< HS Diploma -0.012 0.268 0.413
## prnt.empl.blStay at Home Parent 0.017 -0.030 -0.015
## prnt.empl.blUnemployed 0.010 -0.009 -0.010
## prnt.empl.blOther -0.011 -0.014 -0.033
## neighb_phenx_avg_p.bl.cm 0.042 -0.004 0.061
## overall.income.bl[>=50K & <100K] -0.011 -0.175 -0.276
## overall.income.bl[<50k] -0.079 -0.160 -0.417
## overall.income.bl[Don't Know or Refuse] -0.056 -0.100 -0.253
## sex.blFemale -0.017 0.013 0.022
## reshist_addr1_pm252016aa_bl.c5 -0.027 0.001 -0.017
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.002 0.004 0.001
## h..HSD h..<HD p..aHP
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma 0.381
## prnt.empl.blStay at Home Parent -0.050 -0.095
## prnt.empl.blUnemployed -0.069 -0.098 0.149
## prnt.empl.blOther -0.014 -0.020 0.159
## neighb_phenx_avg_p.bl.cm 0.055 0.049 0.028
## overall.income.bl[>=50K & <100K] -0.173 -0.115 -0.029
## overall.income.bl[<50k] -0.367 -0.310 -0.052
## overall.income.bl[Don't Know or Refuse] -0.241 -0.220 -0.075
## sex.blFemale 0.014 -0.005 -0.006
## reshist_addr1_pm252016aa_bl.c5 -0.009 -0.016 -0.017
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.001 0.006 0.001
## prn..U prn..O n___..
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther 0.134
## neighb_phenx_avg_p.bl.cm 0.023 0.003
## overall.income.bl[>=50K & <100K] -0.014 -0.049 0.080
## overall.income.bl[<50k] -0.101 -0.139 0.151
## overall.income.bl[Don't Know or Refuse] -0.079 -0.099 0.083
## sex.blFemale 0.018 0.017 0.027
## reshist_addr1_pm252016aa_bl.c5 -0.002 -0.001 0.061
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.003 -0.001 0.003
## o..[&< o..[<5 o..KoR
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k] 0.509
## overall.income.bl[Don't Know or Refuse] 0.364 0.490
## sex.blFemale -0.006 -0.007 0.007
## reshist_addr1_pm252016aa_bl.c5 -0.018 -0.030 -0.030
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.006 0.005 0.008
## sx.blF r_1_25
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k]
## overall.income.bl[Don't Know or Refuse]
## sex.blFemale
## reshist_addr1_pm252016aa_bl.c5 -0.004
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.000 0.006
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -3.1937533 -0.7290294 -0.2613143 0.3852753 4.4121102
##
## Number of Observations: 23857
## Number of Groups:
## abcd_site subjectid %in% abcd_site
## 21 9345summary(external_zinb_r$zi.fit)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## NA NA NA
##
## Random effects:
## Formula: ~1 | abcd_site
## (Intercept) Residual
## StdDev: 0.2717397 0.5423831
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: zp ~ reshist_addr1_no2_2016_aavg_bl.c533 * interview_age.c9.y + race_ethnicity.bl + high.educ.bl + prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5
## Value Std.Error
## (Intercept) -4.123617 0.14329048
## reshist_addr1_no2_2016_aavg_bl.c533 -0.011481 0.00878942
## interview_age.c9.y 0.198899 0.04830688
## race_ethnicity.blHispanic 0.201784 0.06369557
## race_ethnicity.blBlack 0.595178 0.06586044
## race_ethnicity.blOther 0.369174 0.06104144
## high.educ.blBachelor 0.103266 0.05365260
## high.educ.blSome College 0.143513 0.06235994
## high.educ.blHS Diploma/GED 0.411105 0.08163599
## high.educ.bl< HS Diploma 0.550172 0.09862945
## prnt.empl.blStay at Home Parent 0.017284 0.05403504
## prnt.empl.blUnemployed 0.023649 0.08290096
## prnt.empl.blOther -0.292708 0.08708412
## neighb_phenx_avg_p.bl.cm 0.202779 0.02330439
## overall.income.bl[>=50K & <100K] -0.200990 0.05602420
## overall.income.bl[<50k] -0.171727 0.06789425
## overall.income.bl[Don't Know or Refuse] 0.164018 0.07694578
## sex.blFemale 0.225122 0.03899144
## reshist_addr1_pm252016aa_bl.c5 0.068361 0.02029657
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.001296 0.00342753
## DF t-value p-value
## (Intercept) 23817 -28.778024 0.0000
## reshist_addr1_no2_2016_aavg_bl.c533 23817 -1.306207 0.1915
## interview_age.c9.y 23817 4.117411 0.0000
## race_ethnicity.blHispanic 23817 3.167943 0.0015
## race_ethnicity.blBlack 23817 9.036962 0.0000
## race_ethnicity.blOther 23817 6.047919 0.0000
## high.educ.blBachelor 23817 1.924719 0.0543
## high.educ.blSome College 23817 2.301370 0.0214
## high.educ.blHS Diploma/GED 23817 5.035831 0.0000
## high.educ.bl< HS Diploma 23817 5.578173 0.0000
## prnt.empl.blStay at Home Parent 23817 0.319862 0.7491
## prnt.empl.blUnemployed 23817 0.285274 0.7754
## prnt.empl.blOther 23817 -3.361211 0.0008
## neighb_phenx_avg_p.bl.cm 23817 8.701331 0.0000
## overall.income.bl[>=50K & <100K] 23817 -3.587558 0.0003
## overall.income.bl[<50k] 23817 -2.529326 0.0114
## overall.income.bl[Don't Know or Refuse] 23817 2.131609 0.0330
## sex.blFemale 23817 5.773635 0.0000
## reshist_addr1_pm252016aa_bl.c5 23817 3.368116 0.0008
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 23817 0.378079 0.7054
## Correlation:
## (Intr) rs_1_2_2016__.533
## reshist_addr1_no2_2016_aavg_bl.c533 -0.714
## interview_age.c9.y -0.668 0.719
## race_ethnicity.blHispanic -0.032 -0.043
## race_ethnicity.blBlack -0.032 -0.063
## race_ethnicity.blOther -0.085 -0.022
## high.educ.blBachelor -0.136 -0.002
## high.educ.blSome College -0.094 0.005
## high.educ.blHS Diploma/GED -0.066 -0.004
## high.educ.bl< HS Diploma -0.014 -0.034
## prnt.empl.blStay at Home Parent -0.065 -0.001
## prnt.empl.blUnemployed -0.018 -0.014
## prnt.empl.blOther -0.024 -0.001
## neighb_phenx_avg_p.bl.cm -0.149 0.066
## overall.income.bl[>=50K & <100K] -0.069 -0.019
## overall.income.bl[<50k] -0.030 -0.023
## overall.income.bl[Don't Know or Refuse] -0.028 -0.019
## sex.blFemale -0.151 0.006
## reshist_addr1_pm252016aa_bl.c5 -0.233 -0.166
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.609 -0.786
## in_.9. rc_t.H rc_t.B
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic 0.002
## race_ethnicity.blBlack 0.011 0.407
## race_ethnicity.blOther 0.006 0.334 0.305
## high.educ.blBachelor -0.013 -0.008 -0.005
## high.educ.blSome College -0.004 -0.116 -0.092
## high.educ.blHS Diploma/GED 0.007 -0.154 -0.151
## high.educ.bl< HS Diploma -0.020 -0.172 -0.087
## prnt.empl.blStay at Home Parent -0.001 0.046 0.104
## prnt.empl.blUnemployed -0.004 0.014 -0.031
## prnt.empl.blOther 0.007 0.041 0.002
## neighb_phenx_avg_p.bl.cm -0.004 0.022 0.128
## overall.income.bl[>=50K & <100K] -0.013 -0.097 -0.080
## overall.income.bl[<50k] -0.011 -0.140 -0.199
## overall.income.bl[Don't Know or Refuse] -0.018 -0.106 -0.140
## sex.blFemale 0.008 -0.003 -0.018
## reshist_addr1_pm252016aa_bl.c5 -0.013 -0.068 -0.027
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.917 0.004 0.002
## rc_t.O hgh..B hg..SC
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor 0.012
## high.educ.blSome College -0.009 0.459
## high.educ.blHS Diploma/GED 0.002 0.361 0.538
## high.educ.bl< HS Diploma 0.001 0.305 0.469
## prnt.empl.blStay at Home Parent 0.021 -0.031 -0.019
## prnt.empl.blUnemployed 0.013 -0.016 -0.009
## prnt.empl.blOther -0.013 -0.018 -0.027
## neighb_phenx_avg_p.bl.cm 0.031 0.001 0.054
## overall.income.bl[>=50K & <100K] -0.020 -0.158 -0.285
## overall.income.bl[<50k] -0.079 -0.150 -0.413
## overall.income.bl[Don't Know or Refuse] -0.064 -0.108 -0.284
## sex.blFemale -0.012 0.012 0.017
## reshist_addr1_pm252016aa_bl.c5 -0.013 0.005 -0.012
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.003 0.012 0.008
## h..HSD h..<HD p..aHP
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma 0.465
## prnt.empl.blStay at Home Parent -0.058 -0.116
## prnt.empl.blUnemployed -0.078 -0.116 0.164
## prnt.empl.blOther -0.011 -0.027 0.140
## neighb_phenx_avg_p.bl.cm 0.051 0.055 0.030
## overall.income.bl[>=50K & <100K] -0.189 -0.135 -0.022
## overall.income.bl[<50k] -0.396 -0.355 -0.048
## overall.income.bl[Don't Know or Refuse] -0.288 -0.269 -0.091
## sex.blFemale 0.012 -0.005 0.004
## reshist_addr1_pm252016aa_bl.c5 0.003 -0.021 -0.009
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.003 0.026 0.010
## prn..U prn..O n___..
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther 0.124
## neighb_phenx_avg_p.bl.cm 0.026 -0.005
## overall.income.bl[>=50K & <100K] -0.012 -0.041 0.073
## overall.income.bl[<50k] -0.088 -0.117 0.133
## overall.income.bl[Don't Know or Refuse] -0.083 -0.098 0.085
## sex.blFemale 0.031 0.013 0.023
## reshist_addr1_pm252016aa_bl.c5 0.003 0.002 0.053
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.008 -0.004 0.003
## o..[&< o..[<5 o..KoR
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k] 0.491
## overall.income.bl[Don't Know or Refuse] 0.389 0.547
## sex.blFemale -0.006 -0.005 0.000
## reshist_addr1_pm252016aa_bl.c5 -0.011 -0.024 -0.036
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.014 0.012 0.024
## sx.blF r_1_25
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k]
## overall.income.bl[Don't Know or Refuse]
## sex.blFemale
## reshist_addr1_pm252016aa_bl.c5 -0.006
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.002 0.013
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -0.8341328 -0.3553729 -0.2842283 0.2311904 17.3410959
##
## Number of Observations: 23857
## Number of Groups: 21anova(external_zinb_r)## numDF denDF F-value
## (Intercept) 1 14510 918.9759
## reshist_addr1_no2_2016_aavg_bl.c533 1 9307 0.3555
## interview_age.c9.y 1 14510 69.7852
## race_ethnicity.bl 3 9307 6.0429
## high.educ.bl 4 9307 31.7884
## prnt.empl.bl 3 9307 15.2829
## neighb_phenx_avg_p.bl.cm 1 9307 92.4746
## overall.income.bl 3 9307 12.2328
## sex.bl 1 9307 139.0900
## reshist_addr1_pm252016aa_bl.c5 1 9307 1.7495
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 1 14510 8.2315
## p-value
## (Intercept) <.0001
## reshist_addr1_no2_2016_aavg_bl.c533 0.5510
## interview_age.c9.y <.0001
## race_ethnicity.bl 0.0004
## high.educ.bl <.0001
## prnt.empl.bl <.0001
## neighb_phenx_avg_p.bl.cm <.0001
## overall.income.bl <.0001
## sex.bl <.0001
## reshist_addr1_pm252016aa_bl.c5 0.1860
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.0041VarCorr(external_zinb_r)## Variance StdDev
## abcd_site = pdLogChol(1)
## (Intercept) 0.01673721 0.1293724
## subjectid = pdLogChol(1)
## (Intercept) 1.20877686 1.0994439
## Residual 1.11937607 1.0580057Assumption checking for ZINB Models
#Check outlier/residuals with this df
external_res <- df_cc
external_res$level1_resid.raw <- residuals(external_zinb_r)
external_res$level1_resid.pearson <- residuals(external_zinb_r, type="pearson")
#Add predicted values (Yhat)
external_res$cbcl_scr_syn_external_r_predicted <- predict(external_zinb_r,external_res,type="response")
#Incidence
external_res$incidence <- estimate.probability(external_res$cbcl_scr_syn_external_r, method="empirical")
#Plotting histogram of residuals, but may be skewed since using ZINB, so make sure to check below plots
hist(external_res$level1_resid.pearson)
### Incidence vs. X’s Plots
#age
ggplot(external_res,aes(incidence,interview_age)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
#pm2.5
ggplot(external_res,aes(incidence,reshist_addr1_no2_2016_aavg_bl)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
### Residuals vs Y (CBCL Outcome) Plot
plot(external_res$level1_resid.pearson, external_res$cbcl_scr_syn_external_r)
### Residuals vs Yhat Plot
plot(external_res$level1_resid.pearson, external_res$cbcl_scr_syn_external_r_predicted)
### Residuals vs Row Plot
plot(as.numeric(rownames(external_res)),external_res$level1_resid.pearson)
### Residuals vs X’s Plots
#age
ggplot(external_res,aes(level1_resid.pearson,interview_age)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
#pm2.5
ggplot(external_res,aes(level1_resid.pearson,reshist_addr1_no2_2016_aavg_bl)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
Anxious/Depressed
CBCL + AP Longitudinal Models
anxdep_zinb_r <- glmm.zinb(cbcl_scr_syn_anxdep_r ~ reshist_addr1_no2_2016_aavg_bl.c533*interview_age.c9.y + race_ethnicity.bl + high.educ.bl+ prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5, random = ~1|abcd_site/subjectid,
zi_fixed = ~ reshist_addr1_no2_2016_aavg_bl.c533*interview_age.c9.y + race_ethnicity.bl + high.educ.bl+ prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5, zi_random = ~1|abcd_site, data = df_cc)## Computational iterations: 11
## Computational time: 1.836 minutessummary(anxdep_zinb_r)## Linear mixed-effects model fit by maximum likelihood
## Data: df_cc
## AIC BIC logLik
## NA NA NA
##
## Random effects:
## Formula: ~1 | abcd_site
## (Intercept)
## StdDev: 0.1233201
##
## Formula: ~1 | subjectid %in% abcd_site
## (Intercept) Residual
## StdDev: 0.9717984 0.9387408
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: cbcl_scr_syn_anxdep_r ~ reshist_addr1_no2_2016_aavg_bl.c533 * interview_age.c9.y + race_ethnicity.bl + high.educ.bl + prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5
## Value Std.Error
## (Intercept) 0.6766333 0.06021954
## reshist_addr1_no2_2016_aavg_bl.c533 -0.0031452 0.00341298
## interview_age.c9.y 0.0007070 0.01288197
## race_ethnicity.blHispanic -0.0126983 0.03628298
## race_ethnicity.blBlack -0.4363446 0.04130931
## race_ethnicity.blOther -0.0967148 0.03679433
## high.educ.blBachelor -0.0290179 0.03065092
## high.educ.blSome College -0.0541162 0.03524018
## high.educ.blHS Diploma/GED -0.2833290 0.05035547
## high.educ.bl< HS Diploma -0.2712099 0.06496668
## prnt.empl.blStay at Home Parent 0.0402540 0.03125643
## prnt.empl.blUnemployed 0.1839800 0.05152906
## prnt.empl.blOther 0.1508501 0.04525466
## neighb_phenx_avg_p.bl.cm -0.1135505 0.01316127
## overall.income.bl[>=50K & <100K] 0.1119089 0.03104746
## overall.income.bl[<50k] 0.1606427 0.03927408
## overall.income.bl[Don't Know or Refuse] 0.0335538 0.04956568
## sex.blFemale 0.0554366 0.02285218
## reshist_addr1_pm252016aa_bl.c5 -0.0186305 0.01158560
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.0018656 0.00088718
## DF t-value p-value
## (Intercept) 14510 11.236109 0.0000
## reshist_addr1_no2_2016_aavg_bl.c533 9307 -0.921531 0.3568
## interview_age.c9.y 14510 0.054881 0.9562
## race_ethnicity.blHispanic 9307 -0.349978 0.7264
## race_ethnicity.blBlack 9307 -10.562862 0.0000
## race_ethnicity.blOther 9307 -2.628525 0.0086
## high.educ.blBachelor 9307 -0.946722 0.3438
## high.educ.blSome College 9307 -1.535639 0.1247
## high.educ.blHS Diploma/GED 9307 -5.626580 0.0000
## high.educ.bl< HS Diploma 9307 -4.174600 0.0000
## prnt.empl.blStay at Home Parent 9307 1.287863 0.1978
## prnt.empl.blUnemployed 9307 3.570412 0.0004
## prnt.empl.blOther 9307 3.333361 0.0009
## neighb_phenx_avg_p.bl.cm 9307 -8.627626 0.0000
## overall.income.bl[>=50K & <100K] 9307 3.604445 0.0003
## overall.income.bl[<50k] 9307 4.090299 0.0000
## overall.income.bl[Don't Know or Refuse] 9307 0.676956 0.4985
## sex.blFemale 9307 2.425876 0.0153
## reshist_addr1_pm252016aa_bl.c5 9307 -1.608077 0.1079
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 14510 -2.102867 0.0355
## Correlation:
## (Intr) rs_1_2_2016__.533
## reshist_addr1_no2_2016_aavg_bl.c533 -0.605
## interview_age.c9.y -0.370 0.421
## race_ethnicity.blHispanic -0.025 -0.050
## race_ethnicity.blBlack -0.021 -0.074
## race_ethnicity.blOther -0.081 -0.027
## high.educ.blBachelor -0.178 0.010
## high.educ.blSome College -0.121 0.018
## high.educ.blHS Diploma/GED -0.071 0.005
## high.educ.bl< HS Diploma -0.025 -0.019
## prnt.empl.blStay at Home Parent -0.082 0.005
## prnt.empl.blUnemployed -0.025 -0.011
## prnt.empl.blOther -0.037 -0.002
## neighb_phenx_avg_p.bl.cm -0.178 0.089
## overall.income.bl[>=50K & <100K] -0.120 -0.011
## overall.income.bl[<50k] -0.059 -0.018
## overall.income.bl[Don't Know or Refuse] -0.055 -0.008
## sex.blFemale -0.184 0.000
## reshist_addr1_pm252016aa_bl.c5 -0.306 -0.236
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.338 -0.457
## in_.9. rc_t.H rc_t.B
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic -0.001
## race_ethnicity.blBlack 0.001 0.344
## race_ethnicity.blOther -0.001 0.285 0.253
## high.educ.blBachelor -0.005 -0.018 -0.012
## high.educ.blSome College 0.001 -0.109 -0.082
## high.educ.blHS Diploma/GED 0.002 -0.141 -0.140
## high.educ.bl< HS Diploma -0.006 -0.165 -0.068
## prnt.empl.blStay at Home Parent 0.003 0.043 0.092
## prnt.empl.blUnemployed -0.002 0.010 -0.040
## prnt.empl.blOther 0.003 0.040 0.010
## neighb_phenx_avg_p.bl.cm -0.005 0.029 0.139
## overall.income.bl[>=50K & <100K] -0.006 -0.092 -0.059
## overall.income.bl[<50k] -0.005 -0.146 -0.179
## overall.income.bl[Don't Know or Refuse] -0.010 -0.096 -0.120
## sex.blFemale 0.001 -0.008 -0.018
## reshist_addr1_pm252016aa_bl.c5 -0.006 -0.081 -0.028
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.919 0.004 0.004
## rc_t.O hgh..B hg..SC
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor 0.000
## high.educ.blSome College -0.024 0.453
## high.educ.blHS Diploma/GED -0.006 0.327 0.488
## high.educ.bl< HS Diploma -0.009 0.258 0.401
## prnt.empl.blStay at Home Parent 0.020 -0.031 -0.015
## prnt.empl.blUnemployed 0.010 -0.009 -0.009
## prnt.empl.blOther -0.011 -0.013 -0.033
## neighb_phenx_avg_p.bl.cm 0.039 -0.005 0.062
## overall.income.bl[>=50K & <100K] -0.015 -0.175 -0.277
## overall.income.bl[<50k] -0.081 -0.160 -0.418
## overall.income.bl[Don't Know or Refuse] -0.061 -0.100 -0.249
## sex.blFemale -0.018 0.015 0.023
## reshist_addr1_pm252016aa_bl.c5 -0.024 -0.001 -0.017
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.002 0.004 0.001
## h..HSD h..<HD p..aHP
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma 0.367
## prnt.empl.blStay at Home Parent -0.053 -0.094
## prnt.empl.blUnemployed -0.069 -0.097 0.147
## prnt.empl.blOther -0.011 -0.020 0.157
## neighb_phenx_avg_p.bl.cm 0.057 0.051 0.027
## overall.income.bl[>=50K & <100K] -0.169 -0.111 -0.031
## overall.income.bl[<50k] -0.362 -0.306 -0.052
## overall.income.bl[Don't Know or Refuse] -0.233 -0.215 -0.076
## sex.blFemale 0.015 -0.003 -0.006
## reshist_addr1_pm252016aa_bl.c5 -0.010 -0.016 -0.016
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.001 0.007 0.002
## prn..U prn..O n___..
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther 0.131
## neighb_phenx_avg_p.bl.cm 0.022 0.004
## overall.income.bl[>=50K & <100K] -0.014 -0.049 0.082
## overall.income.bl[<50k] -0.102 -0.140 0.149
## overall.income.bl[Don't Know or Refuse] -0.077 -0.097 0.083
## sex.blFemale 0.020 0.020 0.027
## reshist_addr1_pm252016aa_bl.c5 -0.002 0.000 0.060
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.003 -0.001 0.004
## o..[&< o..[<5 o..KoR
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k] 0.503
## overall.income.bl[Don't Know or Refuse] 0.355 0.477
## sex.blFemale -0.006 -0.007 0.008
## reshist_addr1_pm252016aa_bl.c5 -0.017 -0.028 -0.029
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.007 0.005 0.010
## sx.blF r_1_25
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k]
## overall.income.bl[Don't Know or Refuse]
## sex.blFemale
## reshist_addr1_pm252016aa_bl.c5 -0.003
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.001 0.007
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.8213955 -0.7603808 -0.2213842 0.4352139 4.4170092
##
## Number of Observations: 23857
## Number of Groups:
## abcd_site subjectid %in% abcd_site
## 21 9345summary(anxdep_zinb_r$zi.fit)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## NA NA NA
##
## Random effects:
## Formula: ~1 | abcd_site
## (Intercept) Residual
## StdDev: 0.4140828 0.431547
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: zp ~ reshist_addr1_no2_2016_aavg_bl.c533 * interview_age.c9.y + race_ethnicity.bl + high.educ.bl + prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5
## Value Std.Error
## (Intercept) -4.947661 0.16498494
## reshist_addr1_no2_2016_aavg_bl.c533 -0.026339 0.00942274
## interview_age.c9.y 0.073993 0.05011007
## race_ethnicity.blHispanic 0.014039 0.06457621
## race_ethnicity.blBlack 1.043904 0.05903589
## race_ethnicity.blOther 0.326024 0.06447837
## high.educ.blBachelor 0.342486 0.05637653
## high.educ.blSome College 0.301923 0.06278417
## high.educ.blHS Diploma/GED 0.542071 0.07652303
## high.educ.bl< HS Diploma 0.918285 0.08541701
## prnt.empl.blStay at Home Parent 0.017966 0.05319993
## prnt.empl.blUnemployed -0.088141 0.07221644
## prnt.empl.blOther 0.055045 0.06771928
## neighb_phenx_avg_p.bl.cm 0.308232 0.02156950
## overall.income.bl[>=50K & <100K] -0.185439 0.06003576
## overall.income.bl[<50k] 0.264104 0.06457005
## overall.income.bl[Don't Know or Refuse] 0.321022 0.07441465
## sex.blFemale -0.312032 0.03803103
## reshist_addr1_pm252016aa_bl.c5 0.116731 0.02029249
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.021647 0.00353137
## DF t-value p-value
## (Intercept) 23817 -29.988562 0.0000
## reshist_addr1_no2_2016_aavg_bl.c533 23817 -2.795220 0.0052
## interview_age.c9.y 23817 1.476611 0.1398
## race_ethnicity.blHispanic 23817 0.217402 0.8279
## race_ethnicity.blBlack 23817 17.682528 0.0000
## race_ethnicity.blOther 23817 5.056324 0.0000
## high.educ.blBachelor 23817 6.074983 0.0000
## high.educ.blSome College 23817 4.808905 0.0000
## high.educ.blHS Diploma/GED 23817 7.083759 0.0000
## high.educ.bl< HS Diploma 23817 10.750605 0.0000
## prnt.empl.blStay at Home Parent 23817 0.337701 0.7356
## prnt.empl.blUnemployed 23817 -1.220505 0.2223
## prnt.empl.blOther 23817 0.812845 0.4163
## neighb_phenx_avg_p.bl.cm 23817 14.290180 0.0000
## overall.income.bl[>=50K & <100K] 23817 -3.088805 0.0020
## overall.income.bl[<50k] 23817 4.090190 0.0000
## overall.income.bl[Don't Know or Refuse] 23817 4.313968 0.0000
## sex.blFemale 23817 -8.204671 0.0000
## reshist_addr1_pm252016aa_bl.c5 23817 5.752443 0.0000
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 23817 6.129937 0.0000
## Correlation:
## (Intr) rs_1_2_2016__.533
## reshist_addr1_no2_2016_aavg_bl.c533 -0.663
## interview_age.c9.y -0.609 0.714
## race_ethnicity.blHispanic -0.027 -0.046
## race_ethnicity.blBlack -0.036 -0.075
## race_ethnicity.blOther -0.081 -0.025
## high.educ.blBachelor -0.143 -0.007
## high.educ.blSome College -0.099 -0.003
## high.educ.blHS Diploma/GED -0.081 -0.007
## high.educ.bl< HS Diploma -0.032 -0.044
## prnt.empl.blStay at Home Parent -0.057 -0.010
## prnt.empl.blUnemployed -0.018 -0.010
## prnt.empl.blOther -0.025 -0.001
## neighb_phenx_avg_p.bl.cm -0.122 0.049
## overall.income.bl[>=50K & <100K] -0.066 -0.025
## overall.income.bl[<50k] -0.039 -0.031
## overall.income.bl[Don't Know or Refuse] -0.035 -0.024
## sex.blFemale -0.096 0.003
## reshist_addr1_pm252016aa_bl.c5 -0.207 -0.159
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.567 -0.784
## in_.9. rc_t.H rc_t.B
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic -0.001
## race_ethnicity.blBlack 0.010 0.496
## race_ethnicity.blOther 0.007 0.361 0.369
## high.educ.blBachelor -0.013 -0.018 -0.002
## high.educ.blSome College -0.001 -0.107 -0.096
## high.educ.blHS Diploma/GED 0.009 -0.144 -0.146
## high.educ.bl< HS Diploma -0.019 -0.175 -0.102
## prnt.empl.blStay at Home Parent -0.006 0.051 0.109
## prnt.empl.blUnemployed -0.003 0.013 -0.012
## prnt.empl.blOther 0.009 0.060 0.013
## neighb_phenx_avg_p.bl.cm 0.001 0.015 0.139
## overall.income.bl[>=50K & <100K] -0.013 -0.104 -0.098
## overall.income.bl[<50k] -0.011 -0.160 -0.213
## overall.income.bl[Don't Know or Refuse] -0.016 -0.119 -0.151
## sex.blFemale 0.005 -0.004 -0.034
## reshist_addr1_pm252016aa_bl.c5 -0.003 -0.062 -0.018
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.928 0.005 0.007
## rc_t.O hgh..B hg..SC
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor 0.004
## high.educ.blSome College -0.013 0.540
## high.educ.blHS Diploma/GED -0.001 0.452 0.625
## high.educ.bl< HS Diploma -0.011 0.410 0.575
## prnt.empl.blStay at Home Parent 0.032 -0.017 -0.009
## prnt.empl.blUnemployed 0.018 -0.021 -0.003
## prnt.empl.blOther -0.001 -0.015 -0.030
## neighb_phenx_avg_p.bl.cm 0.032 0.006 0.049
## overall.income.bl[>=50K & <100K] -0.024 -0.159 -0.275
## overall.income.bl[<50k] -0.081 -0.165 -0.419
## overall.income.bl[Don't Know or Refuse] -0.066 -0.120 -0.293
## sex.blFemale -0.020 0.009 0.009
## reshist_addr1_pm252016aa_bl.c5 0.003 0.010 -0.013
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.007 0.013 0.008
## h..HSD h..<HD p..aHP
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma 0.561
## prnt.empl.blStay at Home Parent -0.052 -0.120
## prnt.empl.blUnemployed -0.084 -0.120 0.185
## prnt.empl.blOther -0.024 -0.029 0.174
## neighb_phenx_avg_p.bl.cm 0.045 0.052 0.041
## overall.income.bl[>=50K & <100K] -0.196 -0.157 -0.021
## overall.income.bl[<50k] -0.394 -0.377 -0.051
## overall.income.bl[Don't Know or Refuse] -0.287 -0.269 -0.086
## sex.blFemale 0.013 -0.013 0.016
## reshist_addr1_pm252016aa_bl.c5 0.009 -0.004 -0.005
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.001 0.028 0.018
## prn..U prn..O n___..
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther 0.160
## neighb_phenx_avg_p.bl.cm 0.030 -0.014
## overall.income.bl[>=50K & <100K] -0.010 -0.039 0.054
## overall.income.bl[<50k] -0.081 -0.128 0.124
## overall.income.bl[Don't Know or Refuse] -0.081 -0.112 0.071
## sex.blFemale 0.038 0.021 0.016
## reshist_addr1_pm252016aa_bl.c5 0.004 0.001 0.044
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.007 -0.007 0.002
## o..[&< o..[<5 o..KoR
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k] 0.553
## overall.income.bl[Don't Know or Refuse] 0.439 0.618
## sex.blFemale 0.005 0.000 -0.004
## reshist_addr1_pm252016aa_bl.c5 -0.012 -0.019 -0.035
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.013 0.013 0.023
## sx.blF r_1_25
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k]
## overall.income.bl[Don't Know or Refuse]
## sex.blFemale
## reshist_addr1_pm252016aa_bl.c5 -0.006
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.001 0.002
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.6237448 -0.3365222 -0.2297828 0.2368846 38.4986943
##
## Number of Observations: 23857
## Number of Groups: 21anova(anxdep_zinb_r)## numDF denDF F-value
## (Intercept) 1 14510 337.4840
## reshist_addr1_no2_2016_aavg_bl.c533 1 9307 6.4051
## interview_age.c9.y 1 14510 22.0767
## race_ethnicity.bl 3 9307 31.6014
## high.educ.bl 4 9307 5.7196
## prnt.empl.bl 3 9307 10.2215
## neighb_phenx_avg_p.bl.cm 1 9307 86.9804
## overall.income.bl 3 9307 7.6772
## sex.bl 1 9307 5.8714
## reshist_addr1_pm252016aa_bl.c5 1 9307 2.5414
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 1 14510 4.4220
## p-value
## (Intercept) <.0001
## reshist_addr1_no2_2016_aavg_bl.c533 0.0114
## interview_age.c9.y <.0001
## race_ethnicity.bl <.0001
## high.educ.bl 0.0001
## prnt.empl.bl <.0001
## neighb_phenx_avg_p.bl.cm <.0001
## overall.income.bl <.0001
## sex.bl 0.0154
## reshist_addr1_pm252016aa_bl.c5 0.1109
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.0355Assumption checking for ZINB Models
#Check outlier/residuals with this df
anxdep_res <- df_cc
anxdep_res$level1_resid.raw <- residuals(anxdep_zinb_r)
anxdep_res$level1_resid.pearson <- residuals(anxdep_zinb_r, type="pearson")
#Add predicted values (Yhat)
anxdep_res$cbcl_scr_syn_anxdep_r_predicted <- predict(anxdep_zinb_r,anxdep_res,type="response")
#Incidence
anxdep_res$incidence <- estimate.probability(anxdep_res$cbcl_scr_syn_anxdep_r, method="empirical")
#Plotting histogram of residuals, but may be skewed since using ZINB, so make sure to check below plots
hist(anxdep_res$level1_resid.pearson)
### Incidence vs. X’s Plots
#age
ggplot(anxdep_res,aes(incidence,interview_age)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
#pm2.5
ggplot(anxdep_res,aes(incidence,reshist_addr1_no2_2016_aavg_bl)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
### Residuals vs Y (CBCL Outcome) Plot
plot(anxdep_res$level1_resid.pearson, anxdep_res$cbcl_scr_syn_anxdep_r)
### Residuals vs Yhat Plot
plot(anxdep_res$level1_resid.pearson, anxdep_res$cbcl_scr_syn_anxdep_r_predicted)
### Residuals vs Row Plot
plot(as.numeric(rownames(anxdep_res)),anxdep_res$level1_resid.pearson)
### Residuals vs X’s Plots
#age
ggplot(anxdep_res,aes(level1_resid.pearson,interview_age)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
#pm2.5
ggplot(anxdep_res,aes(level1_resid.pearson,reshist_addr1_no2_2016_aavg_bl)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
Withdrawn/Depressed
CBCL + AP Longitudinal Models
withdep_zinb_r <- glmm.zinb(cbcl_scr_syn_withdep_r ~ reshist_addr1_no2_2016_aavg_bl.c533*interview_age.c9.y + race_ethnicity.bl + high.educ.bl+ prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5, random = ~1|abcd_site/subjectid,
zi_fixed = ~ reshist_addr1_no2_2016_aavg_bl.c533*interview_age.c9.y + race_ethnicity.bl + high.educ.bl+ prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5, zi_random = ~1|abcd_site, data = df_cc)## Computational iterations: 15
## Computational time: 2.449 minutessummary(withdep_zinb_r)## Linear mixed-effects model fit by maximum likelihood
## Data: df_cc
## AIC BIC logLik
## NA NA NA
##
## Random effects:
## Formula: ~1 | abcd_site
## (Intercept)
## StdDev: 0.1165096
##
## Formula: ~1 | subjectid %in% abcd_site
## (Intercept) Residual
## StdDev: 1.206034 0.8062692
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: cbcl_scr_syn_withdep_r ~ reshist_addr1_no2_2016_aavg_bl.c533 * interview_age.c9.y + race_ethnicity.bl + high.educ.bl + prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5
## Value Std.Error
## (Intercept) -0.8322451 0.07308345
## reshist_addr1_no2_2016_aavg_bl.c533 0.0027451 0.00425703
## interview_age.c9.y 0.1410478 0.01680201
## race_ethnicity.blHispanic -0.0079277 0.04636865
## race_ethnicity.blBlack -0.3077999 0.05232581
## race_ethnicity.blOther 0.0199279 0.04718400
## high.educ.blBachelor 0.0760802 0.04008946
## high.educ.blSome College 0.1944957 0.04547859
## high.educ.blHS Diploma/GED 0.0909375 0.06366743
## high.educ.bl< HS Diploma 0.1295833 0.08124752
## prnt.empl.blStay at Home Parent 0.0787774 0.04012553
## prnt.empl.blUnemployed 0.2378801 0.06480842
## prnt.empl.blOther 0.2796106 0.05706535
## neighb_phenx_avg_p.bl.cm -0.1453092 0.01673648
## overall.income.bl[>=50K & <100K] 0.1598615 0.04040472
## overall.income.bl[<50k] 0.2816591 0.05029271
## overall.income.bl[Don't Know or Refuse] 0.2425207 0.06313611
## sex.blFemale -0.0721907 0.02947925
## reshist_addr1_pm252016aa_bl.c5 -0.0103398 0.01389399
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.0035268 0.00115904
## DF t-value p-value
## (Intercept) 14510 -11.387600 0.0000
## reshist_addr1_no2_2016_aavg_bl.c533 9307 0.644840 0.5190
## interview_age.c9.y 14510 8.394695 0.0000
## race_ethnicity.blHispanic 9307 -0.170971 0.8643
## race_ethnicity.blBlack 9307 -5.882372 0.0000
## race_ethnicity.blOther 9307 0.422345 0.6728
## high.educ.blBachelor 9307 1.897760 0.0578
## high.educ.blSome College 9307 4.276642 0.0000
## high.educ.blHS Diploma/GED 9307 1.428320 0.1532
## high.educ.bl< HS Diploma 9307 1.594920 0.1108
## prnt.empl.blStay at Home Parent 9307 1.963273 0.0496
## prnt.empl.blUnemployed 9307 3.670512 0.0002
## prnt.empl.blOther 9307 4.899831 0.0000
## neighb_phenx_avg_p.bl.cm 9307 -8.682187 0.0000
## overall.income.bl[>=50K & <100K] 9307 3.956506 0.0001
## overall.income.bl[<50k] 9307 5.600396 0.0000
## overall.income.bl[Don't Know or Refuse] 9307 3.841236 0.0001
## sex.blFemale 9307 -2.448864 0.0143
## reshist_addr1_pm252016aa_bl.c5 9307 -0.744193 0.4568
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 14510 -3.042878 0.0023
## Correlation:
## (Intr) rs_1_2_2016__.533
## reshist_addr1_no2_2016_aavg_bl.c533 -0.647
## interview_age.c9.y -0.416 0.460
## race_ethnicity.blHispanic -0.020 -0.050
## race_ethnicity.blBlack -0.021 -0.067
## race_ethnicity.blOther -0.088 -0.021
## high.educ.blBachelor -0.195 0.011
## high.educ.blSome College -0.136 0.021
## high.educ.blHS Diploma/GED -0.080 0.006
## high.educ.bl< HS Diploma -0.033 -0.015
## prnt.empl.blStay at Home Parent -0.087 0.004
## prnt.empl.blUnemployed -0.026 -0.010
## prnt.empl.blOther -0.040 -0.002
## neighb_phenx_avg_p.bl.cm -0.185 0.084
## overall.income.bl[>=50K & <100K] -0.136 -0.009
## overall.income.bl[<50k] -0.072 -0.012
## overall.income.bl[Don't Know or Refuse] -0.065 -0.005
## sex.blFemale -0.191 -0.001
## reshist_addr1_pm252016aa_bl.c5 -0.307 -0.207
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.380 -0.497
## in_.9. rc_t.H rc_t.B
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic -0.001
## race_ethnicity.blBlack 0.001 0.363
## race_ethnicity.blOther 0.000 0.291 0.262
## high.educ.blBachelor -0.004 -0.019 -0.013
## high.educ.blSome College 0.001 -0.113 -0.089
## high.educ.blHS Diploma/GED 0.003 -0.145 -0.149
## high.educ.bl< HS Diploma -0.005 -0.174 -0.076
## prnt.empl.blStay at Home Parent 0.004 0.041 0.091
## prnt.empl.blUnemployed -0.002 0.007 -0.042
## prnt.empl.blOther 0.004 0.042 0.011
## neighb_phenx_avg_p.bl.cm -0.007 0.030 0.140
## overall.income.bl[>=50K & <100K] -0.006 -0.088 -0.058
## overall.income.bl[<50k] -0.005 -0.145 -0.180
## overall.income.bl[Don't Know or Refuse] -0.010 -0.103 -0.125
## sex.blFemale 0.001 -0.008 -0.015
## reshist_addr1_pm252016aa_bl.c5 -0.005 -0.101 -0.046
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.922 0.004 0.004
## rc_t.O hgh..B hg..SC
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor -0.001
## high.educ.blSome College -0.024 0.464
## high.educ.blHS Diploma/GED -0.008 0.341 0.508
## high.educ.bl< HS Diploma -0.009 0.273 0.421
## prnt.empl.blStay at Home Parent 0.017 -0.027 -0.013
## prnt.empl.blUnemployed 0.009 -0.006 -0.007
## prnt.empl.blOther -0.009 -0.011 -0.032
## neighb_phenx_avg_p.bl.cm 0.041 -0.006 0.061
## overall.income.bl[>=50K & <100K] -0.008 -0.174 -0.276
## overall.income.bl[<50k] -0.078 -0.162 -0.418
## overall.income.bl[Don't Know or Refuse] -0.062 -0.102 -0.255
## sex.blFemale -0.018 0.017 0.024
## reshist_addr1_pm252016aa_bl.c5 -0.038 -0.005 -0.023
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.002 0.004 0.000
## h..HSD h..<HD p..aHP
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma 0.388
## prnt.empl.blStay at Home Parent -0.050 -0.094
## prnt.empl.blUnemployed -0.068 -0.098 0.152
## prnt.empl.blOther -0.008 -0.017 0.162
## neighb_phenx_avg_p.bl.cm 0.057 0.051 0.027
## overall.income.bl[>=50K & <100K] -0.172 -0.116 -0.030
## overall.income.bl[<50k] -0.369 -0.312 -0.053
## overall.income.bl[Don't Know or Refuse] -0.243 -0.226 -0.077
## sex.blFemale 0.015 -0.002 -0.005
## reshist_addr1_pm252016aa_bl.c5 -0.014 -0.022 -0.019
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.001 0.007 0.001
## prn..U prn..O n___..
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther 0.135
## neighb_phenx_avg_p.bl.cm 0.018 0.005
## overall.income.bl[>=50K & <100K] -0.017 -0.051 0.079
## overall.income.bl[<50k] -0.099 -0.142 0.150
## overall.income.bl[Don't Know or Refuse] -0.080 -0.099 0.085
## sex.blFemale 0.018 0.020 0.030
## reshist_addr1_pm252016aa_bl.c5 -0.004 -0.001 0.072
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.003 -0.002 0.005
## o..[&< o..[<5 o..KoR
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k] 0.513
## overall.income.bl[Don't Know or Refuse] 0.369 0.497
## sex.blFemale -0.008 -0.006 0.007
## reshist_addr1_pm252016aa_bl.c5 -0.016 -0.031 -0.031
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.007 0.006 0.011
## sx.blF r_1_25
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k]
## overall.income.bl[Don't Know or Refuse]
## sex.blFemale
## reshist_addr1_pm252016aa_bl.c5 -0.005
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.001 0.005
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.7780502 -0.6294234 -0.5046226 0.3875329 4.6697641
##
## Number of Observations: 23857
## Number of Groups:
## abcd_site subjectid %in% abcd_site
## 21 9345summary(withdep_zinb_r$zi.fit)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## NA NA NA
##
## Random effects:
## Formula: ~1 | abcd_site
## (Intercept) Residual
## StdDev: 0.547841 0.2646025
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: zp ~ reshist_addr1_no2_2016_aavg_bl.c533 * interview_age.c9.y + race_ethnicity.bl + high.educ.bl + prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5
## Value Std.Error
## (Intercept) -3.228797 0.13578132
## reshist_addr1_no2_2016_aavg_bl.c533 -0.028256 0.00438699
## interview_age.c9.y -0.456696 0.02661509
## race_ethnicity.blHispanic 0.177564 0.03308146
## race_ethnicity.blBlack 0.560545 0.03594629
## race_ethnicity.blOther 0.008858 0.03535556
## high.educ.blBachelor 0.264381 0.02666123
## high.educ.blSome College 0.299023 0.03237184
## high.educ.blHS Diploma/GED 0.180973 0.04899322
## high.educ.bl< HS Diploma 0.620542 0.05834153
## prnt.empl.blStay at Home Parent -0.607917 0.03517397
## prnt.empl.blUnemployed -0.142580 0.05011382
## prnt.empl.blOther -0.035462 0.04305319
## neighb_phenx_avg_p.bl.cm 0.462282 0.01401739
## overall.income.bl[>=50K & <100K] -0.495409 0.02935671
## overall.income.bl[<50k] -0.814584 0.03873933
## overall.income.bl[Don't Know or Refuse] 0.327030 0.03743477
## sex.blFemale 0.211421 0.02084046
## reshist_addr1_pm252016aa_bl.c5 0.067008 0.01223031
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.020050 0.00190085
## DF t-value p-value
## (Intercept) 23817 -23.77939 0.0000
## reshist_addr1_no2_2016_aavg_bl.c533 23817 -6.44089 0.0000
## interview_age.c9.y 23817 -17.15928 0.0000
## race_ethnicity.blHispanic 23817 5.36749 0.0000
## race_ethnicity.blBlack 23817 15.59396 0.0000
## race_ethnicity.blOther 23817 0.25053 0.8022
## high.educ.blBachelor 23817 9.91630 0.0000
## high.educ.blSome College 23817 9.23715 0.0000
## high.educ.blHS Diploma/GED 23817 3.69384 0.0002
## high.educ.bl< HS Diploma 23817 10.63637 0.0000
## prnt.empl.blStay at Home Parent 23817 -17.28314 0.0000
## prnt.empl.blUnemployed 23817 -2.84512 0.0044
## prnt.empl.blOther 23817 -0.82368 0.4101
## neighb_phenx_avg_p.bl.cm 23817 32.97919 0.0000
## overall.income.bl[>=50K & <100K] 23817 -16.87549 0.0000
## overall.income.bl[<50k] 23817 -21.02732 0.0000
## overall.income.bl[Don't Know or Refuse] 23817 8.73599 0.0000
## sex.blFemale 23817 10.14472 0.0000
## reshist_addr1_pm252016aa_bl.c5 23817 5.47881 0.0000
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 23817 10.54815 0.0000
## Correlation:
## (Intr) rs_1_2_2016__.533
## reshist_addr1_no2_2016_aavg_bl.c533 -0.349
## interview_age.c9.y -0.299 0.629
## race_ethnicity.blHispanic -0.021 -0.037
## race_ethnicity.blBlack -0.008 -0.084
## race_ethnicity.blOther -0.042 -0.025
## high.educ.blBachelor -0.076 -0.005
## high.educ.blSome College -0.053 0.003
## high.educ.blHS Diploma/GED -0.031 -0.008
## high.educ.bl< HS Diploma 0.000 -0.045
## prnt.empl.blStay at Home Parent -0.029 0.000
## prnt.empl.blUnemployed -0.008 -0.016
## prnt.empl.blOther -0.012 -0.009
## neighb_phenx_avg_p.bl.cm -0.095 0.077
## overall.income.bl[>=50K & <100K] -0.025 -0.021
## overall.income.bl[<50k] -0.008 -0.029
## overall.income.bl[Don't Know or Refuse] -0.008 -0.023
## sex.blFemale -0.080 0.004
## reshist_addr1_pm252016aa_bl.c5 -0.153 -0.200
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.279 -0.705
## in_.9. rc_t.H rc_t.B
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic 0.017
## race_ethnicity.blBlack 0.008 0.354
## race_ethnicity.blOther 0.010 0.278 0.230
## high.educ.blBachelor -0.015 -0.007 -0.002
## high.educ.blSome College -0.003 -0.116 -0.107
## high.educ.blHS Diploma/GED 0.009 -0.134 -0.141
## high.educ.bl< HS Diploma -0.021 -0.155 -0.074
## prnt.empl.blStay at Home Parent 0.001 0.039 0.084
## prnt.empl.blUnemployed -0.011 0.012 -0.025
## prnt.empl.blOther 0.001 0.036 -0.012
## neighb_phenx_avg_p.bl.cm -0.006 0.013 0.098
## overall.income.bl[>=50K & <100K] -0.006 -0.103 -0.088
## overall.income.bl[<50k] -0.008 -0.123 -0.192
## overall.income.bl[Don't Know or Refuse] -0.016 -0.112 -0.168
## sex.blFemale -0.001 -0.005 -0.015
## reshist_addr1_pm252016aa_bl.c5 -0.013 -0.042 -0.008
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.917 -0.012 0.004
## rc_t.O hgh..B hg..SC
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor 0.014
## high.educ.blSome College 0.006 0.438
## high.educ.blHS Diploma/GED 0.004 0.298 0.439
## high.educ.bl< HS Diploma 0.007 0.256 0.396
## prnt.empl.blStay at Home Parent 0.020 -0.036 -0.020
## prnt.empl.blUnemployed 0.016 -0.023 -0.017
## prnt.empl.blOther -0.023 -0.028 -0.042
## neighb_phenx_avg_p.bl.cm 0.029 -0.004 0.047
## overall.income.bl[>=50K & <100K] -0.023 -0.140 -0.275
## overall.income.bl[<50k] -0.060 -0.114 -0.362
## overall.income.bl[Don't Know or Refuse] -0.067 -0.094 -0.279
## sex.blFemale -0.009 0.009 0.018
## reshist_addr1_pm252016aa_bl.c5 0.004 0.011 0.002
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.007 0.009 0.002
## h..HSD h..<HD p..aHP
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma 0.353
## prnt.empl.blStay at Home Parent -0.046 -0.103
## prnt.empl.blUnemployed -0.066 -0.127 0.112
## prnt.empl.blOther -0.024 -0.045 0.114
## neighb_phenx_avg_p.bl.cm 0.030 0.039 0.012
## overall.income.bl[>=50K & <100K] -0.160 -0.106 -0.019
## overall.income.bl[<50k] -0.323 -0.316 -0.028
## overall.income.bl[Don't Know or Refuse] -0.271 -0.267 -0.076
## sex.blFemale 0.006 -0.014 0.000
## reshist_addr1_pm252016aa_bl.c5 0.014 -0.020 0.001
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.004 0.028 0.006
## prn..U prn..O n___..
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther 0.115
## neighb_phenx_avg_p.bl.cm 0.030 -0.018
## overall.income.bl[>=50K & <100K] -0.009 -0.048 0.066
## overall.income.bl[<50k] -0.077 -0.115 0.109
## overall.income.bl[Don't Know or Refuse] -0.079 -0.107 0.070
## sex.blFemale 0.029 0.010 0.028
## reshist_addr1_pm252016aa_bl.c5 0.004 0.018 0.035
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.014 0.001 0.004
## o..[&< o..[<5 o..KoR
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k] 0.405
## overall.income.bl[Don't Know or Refuse] 0.366 0.490
## sex.blFemale -0.019 -0.015 0.000
## reshist_addr1_pm252016aa_bl.c5 -0.017 -0.016 -0.038
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.009 0.008 0.019
## sx.blF r_1_25
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k]
## overall.income.bl[Don't Know or Refuse]
## sex.blFemale
## reshist_addr1_pm252016aa_bl.c5 -0.011
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.003 0.011
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.6396779 -0.4962796 0.1665693 0.2654016 42.1463544
##
## Number of Observations: 23857
## Number of Groups: 21anova(withdep_zinb_r)## numDF denDF F-value
## (Intercept) 1 14510 228.46133
## reshist_addr1_no2_2016_aavg_bl.c533 1 9307 0.51912
## interview_age.c9.y 1 14510 202.46829
## race_ethnicity.bl 3 9307 7.97398
## high.educ.bl 4 9307 28.21119
## prnt.empl.bl 3 9307 18.02121
## neighb_phenx_avg_p.bl.cm 1 9307 91.30046
## overall.income.bl 3 9307 11.31976
## sex.bl 1 9307 6.00063
## reshist_addr1_pm252016aa_bl.c5 1 9307 0.53123
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 1 14510 9.25910
## p-value
## (Intercept) <.0001
## reshist_addr1_no2_2016_aavg_bl.c533 0.4712
## interview_age.c9.y <.0001
## race_ethnicity.bl <.0001
## high.educ.bl <.0001
## prnt.empl.bl <.0001
## neighb_phenx_avg_p.bl.cm <.0001
## overall.income.bl <.0001
## sex.bl 0.0143
## reshist_addr1_pm252016aa_bl.c5 0.4661
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.0023Assumption checking for ZINB Models
#Check outlier/residuals with this df
withdep_res <- df_cc
withdep_res$level1_resid.raw <- residuals(withdep_zinb_r)
withdep_res$level1_resid.pearson <- residuals(withdep_zinb_r, type="pearson")
#Add predicted values (Yhat)
withdep_res$cbcl_scr_syn_withdep_r_predicted <- predict(withdep_zinb_r,withdep_res,type="response")
#Incidence
withdep_res$incidence <- estimate.probability(withdep_res$cbcl_scr_syn_withdep_r, method="empirical")
#Plotting histogram of residuals, but may be skewed since using ZINB, so make sure to check below plots
hist(withdep_res$level1_resid.pearson)
### Incidence vs. X’s Plots
#age
ggplot(withdep_res,aes(incidence,interview_age)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 7.4176e-05## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 1.0709e-14## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 1.3755e-09## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 7.4176e-05## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.0709e-14## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1.3755e-09
#pm2.5
ggplot(withdep_res,aes(incidence,reshist_addr1_no2_2016_aavg_bl)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 7.4176e-05## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 1.0709e-14## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 1.3755e-09## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 7.4176e-05## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.0709e-14## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1.3755e-09
### Residuals vs Y (CBCL Outcome) Plot
plot(withdep_res$level1_resid.pearson, withdep_res$cbcl_scr_syn_withdep_r)
### Residuals vs Yhat Plot
plot(withdep_res$level1_resid.pearson, withdep_res$cbcl_scr_syn_withdep_r_predicted)
### Residuals vs Row Plot
plot(as.numeric(rownames(withdep_res)),withdep_res$level1_resid.pearson)
### Residuals vs X’s Plots
#age
ggplot(withdep_res,aes(level1_resid.pearson,interview_age)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
#pm2.5
ggplot(withdep_res,aes(level1_resid.pearson,reshist_addr1_no2_2016_aavg_bl)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
Attention
CBCL + AP Longitudinal Models
attention_zinb_r <- glmm.zinb(cbcl_scr_syn_attention_r ~ reshist_addr1_no2_2016_aavg_bl.c533*interview_age.c9.y + race_ethnicity.bl + high.educ.bl+ prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5, random = ~1|abcd_site/subjectid,
zi_fixed = ~ reshist_addr1_no2_2016_aavg_bl.c533*interview_age.c9.y + race_ethnicity.bl + high.educ.bl+ prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5, zi_random = ~1|abcd_site, data = df_cc)## Computational iterations: 12
## Computational time: 2.036 minutessummary(attention_zinb_r)## Linear mixed-effects model fit by maximum likelihood
## Data: df_cc
## AIC BIC logLik
## NA NA NA
##
## Random effects:
## Formula: ~1 | abcd_site
## (Intercept)
## StdDev: 0.1343906
##
## Formula: ~1 | subjectid %in% abcd_site
## (Intercept) Residual
## StdDev: 1.141652 0.9027036
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: cbcl_scr_syn_attention_r ~ reshist_addr1_no2_2016_aavg_bl.c533 * interview_age.c9.y + race_ethnicity.bl + high.educ.bl + prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5
## Value Std.Error
## (Intercept) 0.6918714 0.06649810
## reshist_addr1_no2_2016_aavg_bl.c533 -0.0010135 0.00373230
## interview_age.c9.y 0.0054682 0.01186184
## race_ethnicity.blHispanic -0.0495748 0.04153798
## race_ethnicity.blBlack -0.0504005 0.04593407
## race_ethnicity.blOther 0.0646560 0.04199822
## high.educ.blBachelor 0.1526911 0.03532260
## high.educ.blSome College 0.1932789 0.04036654
## high.educ.blHS Diploma/GED 0.0641107 0.05665449
## high.educ.bl< HS Diploma 0.0367784 0.07355647
## prnt.empl.blStay at Home Parent -0.0565876 0.03595366
## prnt.empl.blUnemployed 0.1399176 0.05817749
## prnt.empl.blOther 0.1854585 0.05121404
## neighb_phenx_avg_p.bl.cm -0.1196583 0.01496391
## overall.income.bl[>=50K & <100K] 0.0812604 0.03576919
## overall.income.bl[<50k] 0.1552869 0.04480093
## overall.income.bl[Don't Know or Refuse] 0.0473188 0.05619217
## sex.blFemale -0.4356439 0.02619594
## reshist_addr1_pm252016aa_bl.c5 -0.0090996 0.01314393
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.0030732 0.00082256
## DF t-value p-value
## (Intercept) 14510 10.404379 0.0000
## reshist_addr1_no2_2016_aavg_bl.c533 9307 -0.271535 0.7860
## interview_age.c9.y 14510 0.460993 0.6448
## race_ethnicity.blHispanic 9307 -1.193481 0.2327
## race_ethnicity.blBlack 9307 -1.097235 0.2726
## race_ethnicity.blOther 9307 1.539495 0.1237
## high.educ.blBachelor 9307 4.322758 0.0000
## high.educ.blSome College 9307 4.788097 0.0000
## high.educ.blHS Diploma/GED 9307 1.131608 0.2578
## high.educ.bl< HS Diploma 9307 0.500003 0.6171
## prnt.empl.blStay at Home Parent 9307 -1.573903 0.1155
## prnt.empl.blUnemployed 9307 2.405013 0.0162
## prnt.empl.blOther 9307 3.621243 0.0003
## neighb_phenx_avg_p.bl.cm 9307 -7.996456 0.0000
## overall.income.bl[>=50K & <100K] 9307 2.271799 0.0231
## overall.income.bl[<50k] 9307 3.466154 0.0005
## overall.income.bl[Don't Know or Refuse] 9307 0.842089 0.3998
## sex.blFemale 9307 -16.630203 0.0000
## reshist_addr1_pm252016aa_bl.c5 9307 -0.692307 0.4888
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 14510 -3.736148 0.0002
## Correlation:
## (Intr) rs_1_2_2016__.533
## reshist_addr1_no2_2016_aavg_bl.c533 -0.593
## interview_age.c9.y -0.306 0.355
## race_ethnicity.blHispanic -0.026 -0.055
## race_ethnicity.blBlack -0.022 -0.078
## race_ethnicity.blOther -0.086 -0.030
## high.educ.blBachelor -0.191 0.010
## high.educ.blSome College -0.129 0.017
## high.educ.blHS Diploma/GED -0.076 0.004
## high.educ.bl< HS Diploma -0.030 -0.019
## prnt.empl.blStay at Home Parent -0.085 0.006
## prnt.empl.blUnemployed -0.024 -0.012
## prnt.empl.blOther -0.039 -0.001
## neighb_phenx_avg_p.bl.cm -0.185 0.092
## overall.income.bl[>=50K & <100K] -0.128 -0.009
## overall.income.bl[<50k] -0.064 -0.015
## overall.income.bl[Don't Know or Refuse] -0.061 -0.004
## sex.blFemale -0.182 0.000
## reshist_addr1_pm252016aa_bl.c5 -0.321 -0.235
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.281 -0.385
## in_.9. rc_t.H rc_t.B
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic 0.000
## race_ethnicity.blBlack 0.002 0.356
## race_ethnicity.blOther 0.000 0.287 0.263
## high.educ.blBachelor -0.004 -0.017 -0.010
## high.educ.blSome College -0.001 -0.106 -0.079
## high.educ.blHS Diploma/GED 0.001 -0.139 -0.143
## high.educ.bl< HS Diploma -0.005 -0.164 -0.072
## prnt.empl.blStay at Home Parent 0.001 0.044 0.091
## prnt.empl.blUnemployed -0.002 0.010 -0.041
## prnt.empl.blOther 0.002 0.041 0.009
## neighb_phenx_avg_p.bl.cm -0.004 0.028 0.138
## overall.income.bl[>=50K & <100K] -0.006 -0.089 -0.061
## overall.income.bl[<50k] -0.004 -0.143 -0.184
## overall.income.bl[Don't Know or Refuse] -0.007 -0.097 -0.126
## sex.blFemale 0.002 -0.006 -0.017
## reshist_addr1_pm252016aa_bl.c5 -0.005 -0.083 -0.032
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.922 0.002 0.002
## rc_t.O hgh..B hg..SC
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor 0.000
## high.educ.blSome College -0.021 0.460
## high.educ.blHS Diploma/GED -0.006 0.338 0.503
## high.educ.bl< HS Diploma -0.009 0.265 0.410
## prnt.empl.blStay at Home Parent 0.016 -0.026 -0.013
## prnt.empl.blUnemployed 0.010 -0.010 -0.010
## prnt.empl.blOther -0.014 -0.011 -0.033
## neighb_phenx_avg_p.bl.cm 0.041 -0.003 0.062
## overall.income.bl[>=50K & <100K] -0.011 -0.173 -0.278
## overall.income.bl[<50k] -0.080 -0.161 -0.420
## overall.income.bl[Don't Know or Refuse] -0.057 -0.101 -0.254
## sex.blFemale -0.016 0.014 0.021
## reshist_addr1_pm252016aa_bl.c5 -0.027 0.000 -0.017
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.001 0.003 0.001
## h..HSD h..<HD p..aHP
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma 0.379
## prnt.empl.blStay at Home Parent -0.050 -0.092
## prnt.empl.blUnemployed -0.069 -0.099 0.148
## prnt.empl.blOther -0.013 -0.020 0.158
## neighb_phenx_avg_p.bl.cm 0.057 0.053 0.026
## overall.income.bl[>=50K & <100K] -0.175 -0.114 -0.032
## overall.income.bl[<50k] -0.369 -0.310 -0.054
## overall.income.bl[Don't Know or Refuse] -0.241 -0.219 -0.078
## sex.blFemale 0.014 -0.003 -0.005
## reshist_addr1_pm252016aa_bl.c5 -0.010 -0.016 -0.017
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.002 0.006 0.002
## prn..U prn..O n___..
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther 0.133
## neighb_phenx_avg_p.bl.cm 0.019 0.002
## overall.income.bl[>=50K & <100K] -0.016 -0.050 0.082
## overall.income.bl[<50k] -0.101 -0.138 0.150
## overall.income.bl[Don't Know or Refuse] -0.079 -0.099 0.084
## sex.blFemale 0.019 0.020 0.029
## reshist_addr1_pm252016aa_bl.c5 -0.002 -0.001 0.061
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.003 -0.001 0.003
## o..[&< o..[<5 o..KoR
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k] 0.508
## overall.income.bl[Don't Know or Refuse] 0.363 0.488
## sex.blFemale -0.006 -0.008 0.006
## reshist_addr1_pm252016aa_bl.c5 -0.017 -0.029 -0.030
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.006 0.004 0.008
## sx.blF r_1_25
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k]
## overall.income.bl[Don't Know or Refuse]
## sex.blFemale
## reshist_addr1_pm252016aa_bl.c5 -0.003
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.000 0.005
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.5254727 -0.7140570 -0.2849287 0.4220626 3.6485327
##
## Number of Observations: 23857
## Number of Groups:
## abcd_site subjectid %in% abcd_site
## 21 9345summary(attention_zinb_r$zi.fit)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## NA NA NA
##
## Random effects:
## Formula: ~1 | abcd_site
## (Intercept) Residual
## StdDev: 0.4941459 0.4157335
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: zp ~ reshist_addr1_no2_2016_aavg_bl.c533 * interview_age.c9.y + race_ethnicity.bl + high.educ.bl + prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5
## Value Std.Error
## (Intercept) -5.342355 0.17592724
## reshist_addr1_no2_2016_aavg_bl.c533 -0.000070 0.00921206
## interview_age.c9.y 0.295496 0.05056248
## race_ethnicity.blHispanic 0.226115 0.06875477
## race_ethnicity.blBlack 0.707223 0.06715620
## race_ethnicity.blOther 0.384733 0.06743834
## high.educ.blBachelor 0.001124 0.06056197
## high.educ.blSome College 0.216711 0.06798804
## high.educ.blHS Diploma/GED 0.882153 0.08218212
## high.educ.bl< HS Diploma 1.770432 0.08792922
## prnt.empl.blStay at Home Parent 0.102400 0.05321638
## prnt.empl.blUnemployed -0.380456 0.09020364
## prnt.empl.blOther -0.245772 0.08651725
## neighb_phenx_avg_p.bl.cm 0.406164 0.02452240
## overall.income.bl[>=50K & <100K] -0.325054 0.06254108
## overall.income.bl[<50k] -0.060002 0.07165680
## overall.income.bl[Don't Know or Refuse] 0.073908 0.08229295
## sex.blFemale 0.725284 0.04188048
## reshist_addr1_pm252016aa_bl.c5 0.077735 0.02224135
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.012898 0.00363482
## DF t-value p-value
## (Intercept) 23817 -30.366842 0.0000
## reshist_addr1_no2_2016_aavg_bl.c533 23817 -0.007630 0.9939
## interview_age.c9.y 23817 5.844185 0.0000
## race_ethnicity.blHispanic 23817 3.288722 0.0010
## race_ethnicity.blBlack 23817 10.531023 0.0000
## race_ethnicity.blOther 23817 5.704965 0.0000
## high.educ.blBachelor 23817 0.018563 0.9852
## high.educ.blSome College 23817 3.187482 0.0014
## high.educ.blHS Diploma/GED 23817 10.734129 0.0000
## high.educ.bl< HS Diploma 23817 20.134742 0.0000
## prnt.empl.blStay at Home Parent 23817 1.924225 0.0543
## prnt.empl.blUnemployed 23817 -4.217745 0.0000
## prnt.empl.blOther 23817 -2.840730 0.0045
## neighb_phenx_avg_p.bl.cm 23817 16.562961 0.0000
## overall.income.bl[>=50K & <100K] 23817 -5.197454 0.0000
## overall.income.bl[<50k] 23817 -0.837347 0.4024
## overall.income.bl[Don't Know or Refuse] 23817 0.898107 0.3691
## sex.blFemale 23817 17.317950 0.0000
## reshist_addr1_pm252016aa_bl.c5 23817 3.495059 0.0005
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 23817 -3.548481 0.0004
## Correlation:
## (Intr) rs_1_2_2016__.533
## reshist_addr1_no2_2016_aavg_bl.c533 -0.603
## interview_age.c9.y -0.573 0.734
## race_ethnicity.blHispanic -0.043 -0.042
## race_ethnicity.blBlack -0.037 -0.064
## race_ethnicity.blOther -0.082 -0.027
## high.educ.blBachelor -0.118 -0.009
## high.educ.blSome College -0.085 0.003
## high.educ.blHS Diploma/GED -0.068 -0.007
## high.educ.bl< HS Diploma -0.026 -0.051
## prnt.empl.blStay at Home Parent -0.063 0.001
## prnt.empl.blUnemployed -0.020 -0.009
## prnt.empl.blOther -0.023 -0.005
## neighb_phenx_avg_p.bl.cm -0.129 0.060
## overall.income.bl[>=50K & <100K] -0.057 -0.018
## overall.income.bl[<50k] -0.028 -0.031
## overall.income.bl[Don't Know or Refuse] -0.022 -0.023
## sex.blFemale -0.154 0.003
## reshist_addr1_pm252016aa_bl.c5 -0.206 -0.187
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.517 -0.786
## in_.9. rc_t.H rc_t.B
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic 0.006
## race_ethnicity.blBlack 0.025 0.449
## race_ethnicity.blOther 0.016 0.357 0.342
## high.educ.blBachelor -0.025 -0.008 0.002
## high.educ.blSome College -0.012 -0.103 -0.083
## high.educ.blHS Diploma/GED 0.003 -0.156 -0.148
## high.educ.bl< HS Diploma -0.025 -0.187 -0.105
## prnt.empl.blStay at Home Parent -0.006 0.045 0.096
## prnt.empl.blUnemployed 0.000 0.017 -0.032
## prnt.empl.blOther 0.006 0.046 -0.007
## neighb_phenx_avg_p.bl.cm 0.000 0.016 0.116
## overall.income.bl[>=50K & <100K] -0.008 -0.088 -0.091
## overall.income.bl[<50k] -0.005 -0.152 -0.210
## overall.income.bl[Don't Know or Refuse] -0.012 -0.120 -0.154
## sex.blFemale 0.005 -0.001 -0.018
## reshist_addr1_pm252016aa_bl.c5 -0.031 -0.047 -0.012
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.918 0.000 -0.015
## rc_t.O hgh..B hg..SC
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor 0.016
## high.educ.blSome College -0.009 0.463
## high.educ.blHS Diploma/GED -0.001 0.391 0.587
## high.educ.bl< HS Diploma -0.003 0.369 0.574
## prnt.empl.blStay at Home Parent 0.032 -0.030 -0.014
## prnt.empl.blUnemployed -0.002 -0.017 -0.004
## prnt.empl.blOther -0.018 -0.016 -0.018
## neighb_phenx_avg_p.bl.cm 0.026 -0.007 0.032
## overall.income.bl[>=50K & <100K] -0.037 -0.154 -0.302
## overall.income.bl[<50k] -0.092 -0.151 -0.429
## overall.income.bl[Don't Know or Refuse] -0.078 -0.113 -0.311
## sex.blFemale -0.012 0.006 0.009
## reshist_addr1_pm252016aa_bl.c5 0.008 0.012 -0.016
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.012 0.023 0.017
## h..HSD h..<HD p..aHP
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma 0.602
## prnt.empl.blStay at Home Parent -0.049 -0.111
## prnt.empl.blUnemployed -0.060 -0.110 0.171
## prnt.empl.blOther -0.006 -0.043 0.159
## neighb_phenx_avg_p.bl.cm 0.037 0.062 0.040
## overall.income.bl[>=50K & <100K] -0.229 -0.192 -0.020
## overall.income.bl[<50k] -0.444 -0.448 -0.043
## overall.income.bl[Don't Know or Refuse] -0.343 -0.358 -0.087
## sex.blFemale 0.008 -0.001 0.012
## reshist_addr1_pm252016aa_bl.c5 0.014 -0.004 -0.004
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.007 0.032 0.016
## prn..U prn..O n___..
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther 0.127
## neighb_phenx_avg_p.bl.cm 0.031 -0.013
## overall.income.bl[>=50K & <100K] -0.008 -0.043 0.055
## overall.income.bl[<50k] -0.054 -0.115 0.123
## overall.income.bl[Don't Know or Refuse] -0.057 -0.094 0.072
## sex.blFemale 0.035 0.011 0.018
## reshist_addr1_pm252016aa_bl.c5 0.004 0.015 0.036
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.003 -0.002 -0.001
## o..[&< o..[<5 o..KoR
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k] 0.512
## overall.income.bl[Don't Know or Refuse] 0.412 0.620
## sex.blFemale -0.016 -0.008 -0.013
## reshist_addr1_pm252016aa_bl.c5 -0.010 -0.014 -0.036
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.011 0.007 0.022
## sx.blF r_1_25
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k]
## overall.income.bl[Don't Know or Refuse]
## sex.blFemale
## reshist_addr1_pm252016aa_bl.c5 -0.013
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.002 0.034
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.4743983 -0.2995141 -0.2050831 0.1868958 33.1131250
##
## Number of Observations: 23857
## Number of Groups: 21anova(attention_zinb_r)## numDF denDF F-value
## (Intercept) 1 14510 307.37217
## reshist_addr1_no2_2016_aavg_bl.c533 1 9307 0.06255
## interview_age.c9.y 1 14510 59.40404
## race_ethnicity.bl 3 9307 4.88782
## high.educ.bl 4 9307 21.70167
## prnt.empl.bl 3 9307 11.45021
## neighb_phenx_avg_p.bl.cm 1 9307 65.16174
## overall.income.bl 3 9307 4.08005
## sex.bl 1 9307 276.63212
## reshist_addr1_pm252016aa_bl.c5 1 9307 0.45290
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 1 14510 13.95880
## p-value
## (Intercept) <.0001
## reshist_addr1_no2_2016_aavg_bl.c533 0.8025
## interview_age.c9.y <.0001
## race_ethnicity.bl 0.0021
## high.educ.bl <.0001
## prnt.empl.bl <.0001
## neighb_phenx_avg_p.bl.cm <.0001
## overall.income.bl 0.0066
## sex.bl <.0001
## reshist_addr1_pm252016aa_bl.c5 0.5010
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.0002Assumption checking for ZINB Models
#Check outlier/residuals with this df
attention_res <- df_cc
attention_res$level1_resid.raw <- residuals(attention_zinb_r)
attention_res$level1_resid.pearson <- residuals(attention_zinb_r, type="pearson")
#Add predicted values (Yhat)
attention_res$cbcl_scr_syn_attention_r_predicted <- predict(attention_zinb_r,attention_res,type="response")
#Incidence
attention_res$incidence <- estimate.probability(attention_res$cbcl_scr_syn_attention_r, method="empirical")
#Plotting histogram of residuals, but may be skewed since using ZINB, so make sure to check below plots
hist(attention_res$level1_resid.pearson)
### Incidence vs. X’s Plots
#age
ggplot(attention_res,aes(incidence,interview_age)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
#pm2.5
ggplot(attention_res,aes(incidence,reshist_addr1_no2_2016_aavg_bl)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
### Residuals vs Y (CBCL Outcome) Plot
plot(attention_res$level1_resid.pearson, attention_res$cbcl_scr_syn_attention_r)
### Residuals vs Yhat Plot
plot(attention_res$level1_resid.pearson, attention_res$cbcl_scr_syn_attention_r_predicted)
### Residuals vs Row Plot
plot(as.numeric(rownames(attention_res)),attention_res$level1_resid.pearson)
### Residuals vs X’s Plots
#age
ggplot(attention_res,aes(level1_resid.pearson,interview_age)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
#pm2.5
ggplot(attention_res,aes(level1_resid.pearson,reshist_addr1_no2_2016_aavg_bl)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
Rulebreak
CBCL + AP Longitudinal Models
rulebreak_zinb_r <- glmm.zinb(cbcl_scr_syn_rulebreak_r ~ reshist_addr1_no2_2016_aavg_bl.c533*interview_age.c9.y + race_ethnicity.bl + high.educ.bl+ prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5, random = ~1|abcd_site/subjectid,
zi_fixed = ~ reshist_addr1_no2_2016_aavg_bl.c533*interview_age.c9.y + race_ethnicity.bl + high.educ.bl+ prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5, zi_random = ~1|abcd_site, data = df_cc)## Computational iterations: 14
## Computational time: 2.159 minutessummary(rulebreak_zinb_r)## Linear mixed-effects model fit by maximum likelihood
## Data: df_cc
## AIC BIC logLik
## NA NA NA
##
## Random effects:
## Formula: ~1 | abcd_site
## (Intercept)
## StdDev: 0.1321049
##
## Formula: ~1 | subjectid %in% abcd_site
## (Intercept) Residual
## StdDev: 1.194599 0.7884133
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: cbcl_scr_syn_rulebreak_r ~ reshist_addr1_no2_2016_aavg_bl.c533 * interview_age.c9.y + race_ethnicity.bl + high.educ.bl + prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5
## Value Std.Error
## (Intercept) -0.4912201 0.07412943
## reshist_addr1_no2_2016_aavg_bl.c533 -0.0005020 0.00427253
## interview_age.c9.y 0.0074437 0.01663129
## race_ethnicity.blHispanic -0.0623206 0.04608818
## race_ethnicity.blBlack 0.1010679 0.05035438
## race_ethnicity.blOther 0.0846763 0.04679435
## high.educ.blBachelor 0.1787266 0.03984533
## high.educ.blSome College 0.3271039 0.04488209
## high.educ.blHS Diploma/GED 0.1886083 0.06240523
## high.educ.bl< HS Diploma 0.2283211 0.08016645
## prnt.empl.blStay at Home Parent -0.0321069 0.04010330
## prnt.empl.blUnemployed 0.1796452 0.06338742
## prnt.empl.blOther 0.2557162 0.05602700
## neighb_phenx_avg_p.bl.cm -0.1234932 0.01652115
## overall.income.bl[>=50K & <100K] 0.1482135 0.04018522
## overall.income.bl[<50k] 0.3300615 0.04962171
## overall.income.bl[Don't Know or Refuse] 0.2590148 0.06203115
## sex.blFemale -0.4232865 0.02924360
## reshist_addr1_pm252016aa_bl.c5 -0.0211510 0.01418141
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.0029204 0.00115567
## DF t-value p-value
## (Intercept) 14510 -6.626520 0.0000
## reshist_addr1_no2_2016_aavg_bl.c533 9307 -0.117483 0.9065
## interview_age.c9.y 14510 0.447570 0.6545
## race_ethnicity.blHispanic 9307 -1.352203 0.1763
## race_ethnicity.blBlack 9307 2.007132 0.0448
## race_ethnicity.blOther 9307 1.809542 0.0704
## high.educ.blBachelor 9307 4.485509 0.0000
## high.educ.blSome College 9307 7.288070 0.0000
## high.educ.blHS Diploma/GED 9307 3.022316 0.0025
## high.educ.bl< HS Diploma 9307 2.848087 0.0044
## prnt.empl.blStay at Home Parent 9307 -0.800604 0.4234
## prnt.empl.blUnemployed 9307 2.834082 0.0046
## prnt.empl.blOther 9307 4.564161 0.0000
## neighb_phenx_avg_p.bl.cm 9307 -7.474856 0.0000
## overall.income.bl[>=50K & <100K] 9307 3.688259 0.0002
## overall.income.bl[<50k] 9307 6.651554 0.0000
## overall.income.bl[Don't Know or Refuse] 9307 4.175560 0.0000
## sex.blFemale 9307 -14.474497 0.0000
## reshist_addr1_pm252016aa_bl.c5 9307 -1.491463 0.1359
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 14510 -2.527054 0.0115
## Correlation:
## (Intr) rs_1_2_2016__.533
## reshist_addr1_no2_2016_aavg_bl.c533 -0.631
## interview_age.c9.y -0.386 0.437
## race_ethnicity.blHispanic -0.019 -0.057
## race_ethnicity.blBlack -0.019 -0.075
## race_ethnicity.blOther -0.085 -0.026
## high.educ.blBachelor -0.195 0.009
## high.educ.blSome College -0.138 0.019
## high.educ.blHS Diploma/GED -0.083 0.006
## high.educ.bl< HS Diploma -0.035 -0.017
## prnt.empl.blStay at Home Parent -0.083 0.002
## prnt.empl.blUnemployed -0.024 -0.013
## prnt.empl.blOther -0.037 -0.003
## neighb_phenx_avg_p.bl.cm -0.183 0.086
## overall.income.bl[>=50K & <100K] -0.133 -0.009
## overall.income.bl[<50k] -0.071 -0.012
## overall.income.bl[Don't Know or Refuse] -0.066 -0.005
## sex.blFemale -0.175 0.001
## reshist_addr1_pm252016aa_bl.c5 -0.312 -0.213
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.354 -0.471
## in_.9. rc_t.H rc_t.B
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic 0.001
## race_ethnicity.blBlack 0.002 0.367
## race_ethnicity.blOther 0.001 0.293 0.273
## high.educ.blBachelor -0.004 -0.020 -0.012
## high.educ.blSome College 0.000 -0.106 -0.080
## high.educ.blHS Diploma/GED 0.003 -0.143 -0.146
## high.educ.bl< HS Diploma -0.007 -0.167 -0.076
## prnt.empl.blStay at Home Parent 0.004 0.043 0.093
## prnt.empl.blUnemployed -0.002 0.012 -0.038
## prnt.empl.blOther 0.003 0.047 0.016
## neighb_phenx_avg_p.bl.cm -0.004 0.034 0.140
## overall.income.bl[>=50K & <100K] -0.008 -0.091 -0.066
## overall.income.bl[<50k] -0.005 -0.146 -0.186
## overall.income.bl[Don't Know or Refuse] -0.010 -0.101 -0.128
## sex.blFemale 0.002 -0.008 -0.019
## reshist_addr1_pm252016aa_bl.c5 -0.006 -0.092 -0.042
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.924 0.002 0.003
## rc_t.O hgh..B hg..SC
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor 0.001
## high.educ.blSome College -0.021 0.473
## high.educ.blHS Diploma/GED -0.008 0.351 0.516
## high.educ.bl< HS Diploma -0.011 0.278 0.423
## prnt.empl.blStay at Home Parent 0.016 -0.030 -0.016
## prnt.empl.blUnemployed 0.010 -0.008 -0.008
## prnt.empl.blOther -0.011 -0.013 -0.034
## neighb_phenx_avg_p.bl.cm 0.046 -0.003 0.061
## overall.income.bl[>=50K & <100K] -0.015 -0.171 -0.275
## overall.income.bl[<50k] -0.080 -0.161 -0.420
## overall.income.bl[Don't Know or Refuse] -0.057 -0.101 -0.258
## sex.blFemale -0.016 0.011 0.022
## reshist_addr1_pm252016aa_bl.c5 -0.035 -0.001 -0.018
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.001 0.004 0.001
## h..HSD h..<HD p..aHP
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma 0.389
## prnt.empl.blStay at Home Parent -0.051 -0.099
## prnt.empl.blUnemployed -0.068 -0.100 0.152
## prnt.empl.blOther -0.016 -0.023 0.162
## neighb_phenx_avg_p.bl.cm 0.054 0.050 0.030
## overall.income.bl[>=50K & <100K] -0.175 -0.116 -0.028
## overall.income.bl[<50k] -0.367 -0.310 -0.050
## overall.income.bl[Don't Know or Refuse] -0.244 -0.222 -0.070
## sex.blFemale 0.014 -0.003 -0.007
## reshist_addr1_pm252016aa_bl.c5 -0.010 -0.016 -0.018
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.001 0.008 0.000
## prn..U prn..O n___..
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther 0.138
## neighb_phenx_avg_p.bl.cm 0.023 0.003
## overall.income.bl[>=50K & <100K] -0.015 -0.049 0.079
## overall.income.bl[<50k] -0.102 -0.140 0.151
## overall.income.bl[Don't Know or Refuse] -0.082 -0.101 0.084
## sex.blFemale 0.014 0.017 0.031
## reshist_addr1_pm252016aa_bl.c5 -0.002 -0.001 0.065
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.003 -0.001 0.003
## o..[&< o..[<5 o..KoR
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k] 0.519
## overall.income.bl[Don't Know or Refuse] 0.375 0.503
## sex.blFemale -0.010 -0.010 0.004
## reshist_addr1_pm252016aa_bl.c5 -0.016 -0.029 -0.029
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.008 0.005 0.010
## sx.blF r_1_25
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k]
## overall.income.bl[Don't Know or Refuse]
## sex.blFemale
## reshist_addr1_pm252016aa_bl.c5 -0.007
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.001 0.006
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -3.0046648 -0.6415376 -0.4926786 0.3911059 3.9448317
##
## Number of Observations: 23857
## Number of Groups:
## abcd_site subjectid %in% abcd_site
## 21 9345summary(rulebreak_zinb_r$zi.fit)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## NA NA NA
##
## Random effects:
## Formula: ~1 | abcd_site
## (Intercept) Residual
## StdDev: 0.4409472 0.2695416
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: zp ~ reshist_addr1_no2_2016_aavg_bl.c533 * interview_age.c9.y + race_ethnicity.bl + high.educ.bl + prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5
## Value Std.Error
## (Intercept) -4.366761 0.12003000
## reshist_addr1_no2_2016_aavg_bl.c533 -0.012384 0.00487815
## interview_age.c9.y 0.081746 0.02577645
## race_ethnicity.blHispanic 0.194187 0.03368794
## race_ethnicity.blBlack -0.182735 0.04617739
## race_ethnicity.blOther -0.055407 0.03467836
## high.educ.blBachelor -0.268501 0.02683815
## high.educ.blSome College -0.649769 0.03657857
## high.educ.blHS Diploma/GED -0.134261 0.04848139
## high.educ.bl< HS Diploma -0.008910 0.05773665
## prnt.empl.blStay at Home Parent 0.004042 0.02893617
## prnt.empl.blUnemployed -0.162720 0.05751080
## prnt.empl.blOther -0.087832 0.04874062
## neighb_phenx_avg_p.bl.cm 0.369939 0.01419617
## overall.income.bl[>=50K & <100K] -0.261636 0.02932583
## overall.income.bl[<50k] -0.073323 0.03837333
## overall.income.bl[Don't Know or Refuse] 0.346592 0.04138709
## sex.blFemale 1.162055 0.02350199
## reshist_addr1_pm252016aa_bl.c5 -0.005680 0.01218632
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.012589 0.00183159
## DF t-value p-value
## (Intercept) 23817 -36.38058 0.0000
## reshist_addr1_no2_2016_aavg_bl.c533 23817 -2.53870 0.0111
## interview_age.c9.y 23817 3.17136 0.0015
## race_ethnicity.blHispanic 23817 5.76430 0.0000
## race_ethnicity.blBlack 23817 -3.95724 0.0001
## race_ethnicity.blOther 23817 -1.59775 0.1101
## high.educ.blBachelor 23817 -10.00446 0.0000
## high.educ.blSome College 23817 -17.76365 0.0000
## high.educ.blHS Diploma/GED 23817 -2.76934 0.0056
## high.educ.bl< HS Diploma 23817 -0.15432 0.8774
## prnt.empl.blStay at Home Parent 23817 0.13969 0.8889
## prnt.empl.blUnemployed 23817 -2.82938 0.0047
## prnt.empl.blOther 23817 -1.80203 0.0716
## neighb_phenx_avg_p.bl.cm 23817 26.05911 0.0000
## overall.income.bl[>=50K & <100K] 23817 -8.92170 0.0000
## overall.income.bl[<50k] 23817 -1.91079 0.0560
## overall.income.bl[Don't Know or Refuse] 23817 8.37439 0.0000
## sex.blFemale 23817 49.44494 0.0000
## reshist_addr1_pm252016aa_bl.c5 23817 -0.46610 0.6412
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 23817 6.87300 0.0000
## Correlation:
## (Intr) rs_1_2_2016__.533
## reshist_addr1_no2_2016_aavg_bl.c533 -0.457
## interview_age.c9.y -0.418 0.682
## race_ethnicity.blHispanic -0.024 -0.028
## race_ethnicity.blBlack -0.014 -0.050
## race_ethnicity.blOther -0.044 -0.018
## high.educ.blBachelor -0.061 -0.003
## high.educ.blSome College -0.032 0.006
## high.educ.blHS Diploma/GED -0.015 -0.010
## high.educ.bl< HS Diploma 0.020 -0.043
## prnt.empl.blStay at Home Parent -0.044 0.008
## prnt.empl.blUnemployed -0.014 -0.006
## prnt.empl.blOther -0.017 -0.003
## neighb_phenx_avg_p.bl.cm -0.115 0.080
## overall.income.bl[>=50K & <100K] -0.046 -0.014
## overall.income.bl[<50k] -0.019 -0.025
## overall.income.bl[Don't Know or Refuse] -0.021 -0.015
## sex.blFemale -0.149 0.012
## reshist_addr1_pm252016aa_bl.c5 -0.154 -0.202
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.384 -0.758
## in_.9. rc_t.H rc_t.B
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic 0.011
## race_ethnicity.blBlack 0.009 0.286
## race_ethnicity.blOther -0.002 0.283 0.179
## high.educ.blBachelor -0.019 -0.005 -0.007
## high.educ.blSome College -0.008 -0.127 -0.085
## high.educ.blHS Diploma/GED -0.001 -0.180 -0.135
## high.educ.bl< HS Diploma -0.029 -0.195 -0.077
## prnt.empl.blStay at Home Parent 0.004 0.050 0.076
## prnt.empl.blUnemployed -0.001 0.002 -0.038
## prnt.empl.blOther 0.008 0.030 -0.011
## neighb_phenx_avg_p.bl.cm -0.001 0.016 0.086
## overall.income.bl[>=50K & <100K] -0.014 -0.090 -0.059
## overall.income.bl[<50k] -0.009 -0.128 -0.147
## overall.income.bl[Don't Know or Refuse] -0.011 -0.095 -0.098
## sex.blFemale 0.013 -0.003 -0.013
## reshist_addr1_pm252016aa_bl.c5 -0.009 -0.049 -0.007
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.914 -0.004 0.000
## rc_t.O hgh..B hg..SC
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor 0.008
## high.educ.blSome College -0.003 0.339
## high.educ.blHS Diploma/GED 0.001 0.267 0.401
## high.educ.bl< HS Diploma 0.010 0.232 0.372
## prnt.empl.blStay at Home Parent 0.014 -0.041 -0.028
## prnt.empl.blUnemployed 0.005 -0.016 -0.015
## prnt.empl.blOther -0.024 -0.030 -0.026
## neighb_phenx_avg_p.bl.cm 0.020 -0.015 0.038
## overall.income.bl[>=50K & <100K] -0.015 -0.145 -0.224
## overall.income.bl[<50k] -0.063 -0.143 -0.379
## overall.income.bl[Don't Know or Refuse] -0.065 -0.092 -0.225
## sex.blFemale -0.015 0.005 0.004
## reshist_addr1_pm252016aa_bl.c5 0.002 0.005 -0.015
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.003 0.015 0.005
## h..HSD h..<HD p..aHP
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma 0.396
## prnt.empl.blStay at Home Parent -0.072 -0.125
## prnt.empl.blUnemployed -0.056 -0.097 0.130
## prnt.empl.blOther 0.004 -0.027 0.136
## neighb_phenx_avg_p.bl.cm 0.038 0.052 0.025
## overall.income.bl[>=50K & <100K] -0.135 -0.090 -0.016
## overall.income.bl[<50k] -0.391 -0.352 -0.021
## overall.income.bl[Don't Know or Refuse] -0.262 -0.238 -0.082
## sex.blFemale -0.002 -0.012 0.000
## reshist_addr1_pm252016aa_bl.c5 -0.004 -0.040 -0.012
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.007 0.034 0.005
## prn..U prn..O n___..
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther 0.097
## neighb_phenx_avg_p.bl.cm 0.025 0.000
## overall.income.bl[>=50K & <100K] -0.009 -0.051 0.090
## overall.income.bl[<50k] -0.071 -0.132 0.114
## overall.income.bl[Don't Know or Refuse] -0.059 -0.097 0.071
## sex.blFemale 0.026 0.010 0.034
## reshist_addr1_pm252016aa_bl.c5 -0.003 0.013 0.041
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.004 -0.007 0.001
## o..[&< o..[<5 o..KoR
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k] 0.385
## overall.income.bl[Don't Know or Refuse] 0.300 0.449
## sex.blFemale -0.014 -0.004 0.014
## reshist_addr1_pm252016aa_bl.c5 0.004 -0.009 -0.024
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.019 0.011 0.015
## sx.blF r_1_25
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k]
## overall.income.bl[Don't Know or Refuse]
## sex.blFemale
## reshist_addr1_pm252016aa_bl.c5 -0.009
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.006 0.008
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.0557332 -0.4567171 0.1607592 0.2683931 43.1419566
##
## Number of Observations: 23857
## Number of Groups: 21anova(rulebreak_zinb_r)## numDF denDF F-value
## (Intercept) 1 14510 174.74978
## reshist_addr1_no2_2016_aavg_bl.c533 1 9307 1.27584
## interview_age.c9.y 1 14510 26.30264
## race_ethnicity.bl 3 9307 30.06986
## high.educ.bl 4 9307 49.62558
## prnt.empl.bl 3 9307 16.73247
## neighb_phenx_avg_p.bl.cm 1 9307 64.61964
## overall.income.bl 3 9307 14.37416
## sex.bl 1 9307 209.72445
## reshist_addr1_pm252016aa_bl.c5 1 9307 2.18185
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 1 14510 6.38600
## p-value
## (Intercept) <.0001
## reshist_addr1_no2_2016_aavg_bl.c533 0.2587
## interview_age.c9.y <.0001
## race_ethnicity.bl <.0001
## high.educ.bl <.0001
## prnt.empl.bl <.0001
## neighb_phenx_avg_p.bl.cm <.0001
## overall.income.bl <.0001
## sex.bl <.0001
## reshist_addr1_pm252016aa_bl.c5 0.1397
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.0115Assumption checking for ZINB Models
#Check outlier/residuals with this df
rulebreak_res <- df_cc
rulebreak_res$level1_resid.raw <- residuals(rulebreak_zinb_r)
rulebreak_res$level1_resid.pearson <- residuals(rulebreak_zinb_r, type="pearson")
#Add predicted values (Yhat)
rulebreak_res$cbcl_scr_syn_rulebreak_r_predicted <- predict(rulebreak_zinb_r,rulebreak_res,type="response")
#Incidence
rulebreak_res$incidence <- estimate.probability(rulebreak_res$cbcl_scr_syn_rulebreak_r, method="empirical")
#Plotting histogram of residuals, but may be skewed since using ZINB, so make sure to check below plots
hist(rulebreak_res$level1_resid.pearson)
### Incidence vs. X’s Plots
#age
ggplot(rulebreak_res,aes(incidence,interview_age)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 7.303e-05## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 1.3261e-14## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 1.3333e-09## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 7.303e-05## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.3261e-14## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1.3333e-09
#pm2.5
ggplot(rulebreak_res,aes(incidence,reshist_addr1_no2_2016_aavg_bl)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 7.303e-05## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 1.3261e-14## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 1.3333e-09## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 7.303e-05## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.3261e-14## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1.3333e-09
### Residuals vs Y (CBCL Outcome) Plot
plot(rulebreak_res$level1_resid.pearson, rulebreak_res$cbcl_scr_syn_rulebreak_r)
### Residuals vs Yhat Plot
plot(rulebreak_res$level1_resid.pearson, rulebreak_res$cbcl_scr_syn_rulebreak_r_predicted)
### Residuals vs Row Plot
plot(as.numeric(rownames(rulebreak_res)),rulebreak_res$level1_resid.pearson)
### Residuals vs X’s Plots
#age
ggplot(rulebreak_res,aes(level1_resid.pearson,interview_age)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
#pm2.5
ggplot(rulebreak_res,aes(level1_resid.pearson,reshist_addr1_no2_2016_aavg_bl)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
Aggressive
CBCL + AP Longitudinal Models
aggressive_zinb_r <- glmm.zinb(cbcl_scr_syn_aggressive_r ~ reshist_addr1_no2_2016_aavg_bl.c533*interview_age.c9.y + race_ethnicity.bl + high.educ.bl+ prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5, random = ~1|abcd_site/subjectid,
zi_fixed = ~ reshist_addr1_no2_2016_aavg_bl.c533*interview_age.c9.y + race_ethnicity.bl + high.educ.bl+ prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5, zi_random = ~1|abcd_site, data = df_cc)## Computational iterations: 11
## Computational time: 1.729 minutessummary(aggressive_zinb_r)## Linear mixed-effects model fit by maximum likelihood
## Data: df_cc
## AIC BIC logLik
## NA NA NA
##
## Random effects:
## Formula: ~1 | abcd_site
## (Intercept)
## StdDev: 0.1351839
##
## Formula: ~1 | subjectid %in% abcd_site
## (Intercept) Residual
## StdDev: 1.145747 0.9761331
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: cbcl_scr_syn_aggressive_r ~ reshist_addr1_no2_2016_aavg_bl.c533 * interview_age.c9.y + race_ethnicity.bl + high.educ.bl + prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5
## Value Std.Error
## (Intercept) 0.6855876 0.06747743
## reshist_addr1_no2_2016_aavg_bl.c533 -0.0009181 0.00380849
## interview_age.c9.y -0.0130245 0.01276624
## race_ethnicity.blHispanic -0.0511886 0.04198411
## race_ethnicity.blBlack -0.1872301 0.04674910
## race_ethnicity.blOther -0.0839604 0.04281985
## high.educ.blBachelor 0.0865743 0.03580181
## high.educ.blSome College 0.1510801 0.04084810
## high.educ.blHS Diploma/GED 0.0255219 0.05734854
## high.educ.bl< HS Diploma 0.0920724 0.07377912
## prnt.empl.blStay at Home Parent 0.0253251 0.03624304
## prnt.empl.blUnemployed 0.2139679 0.05878831
## prnt.empl.blOther 0.1822468 0.05189938
## neighb_phenx_avg_p.bl.cm -0.1279770 0.01512550
## overall.income.bl[>=50K & <100K] 0.1223104 0.03618373
## overall.income.bl[<50k] 0.2493145 0.04527164
## overall.income.bl[Don't Know or Refuse] 0.1376904 0.05688751
## sex.blFemale -0.2495197 0.02648157
## reshist_addr1_pm252016aa_bl.c5 -0.0173275 0.01328447
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.0023343 0.00088651
## DF t-value p-value
## (Intercept) 14510 10.160250 0.0000
## reshist_addr1_no2_2016_aavg_bl.c533 9307 -0.241057 0.8095
## interview_age.c9.y 14510 -1.020233 0.3076
## race_ethnicity.blHispanic 9307 -1.219238 0.2228
## race_ethnicity.blBlack 9307 -4.004998 0.0001
## race_ethnicity.blOther 9307 -1.960782 0.0499
## high.educ.blBachelor 9307 2.418154 0.0156
## high.educ.blSome College 9307 3.698584 0.0002
## high.educ.blHS Diploma/GED 9307 0.445031 0.6563
## high.educ.bl< HS Diploma 9307 1.247947 0.2121
## prnt.empl.blStay at Home Parent 9307 0.698757 0.4847
## prnt.empl.blUnemployed 9307 3.639634 0.0003
## prnt.empl.blOther 9307 3.511540 0.0004
## neighb_phenx_avg_p.bl.cm 9307 -8.461010 0.0000
## overall.income.bl[>=50K & <100K] 9307 3.380260 0.0007
## overall.income.bl[<50k] 9307 5.507079 0.0000
## overall.income.bl[Don't Know or Refuse] 9307 2.420398 0.0155
## sex.blFemale 9307 -9.422388 0.0000
## reshist_addr1_pm252016aa_bl.c5 9307 -1.304342 0.1921
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 14510 -2.633146 0.0085
## Correlation:
## (Intr) rs_1_2_2016__.533
## reshist_addr1_no2_2016_aavg_bl.c533 -0.598
## interview_age.c9.y -0.323 0.374
## race_ethnicity.blHispanic -0.023 -0.055
## race_ethnicity.blBlack -0.020 -0.077
## race_ethnicity.blOther -0.083 -0.030
## high.educ.blBachelor -0.189 0.012
## high.educ.blSome College -0.129 0.019
## high.educ.blHS Diploma/GED -0.078 0.007
## high.educ.bl< HS Diploma -0.030 -0.017
## prnt.empl.blStay at Home Parent -0.084 0.005
## prnt.empl.blUnemployed -0.025 -0.011
## prnt.empl.blOther -0.037 -0.002
## neighb_phenx_avg_p.bl.cm -0.182 0.089
## overall.income.bl[>=50K & <100K] -0.127 -0.011
## overall.income.bl[<50k] -0.064 -0.017
## overall.income.bl[Don't Know or Refuse] -0.060 -0.007
## sex.blFemale -0.183 -0.001
## reshist_addr1_pm252016aa_bl.c5 -0.317 -0.236
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.296 -0.405
## in_.9. rc_t.H rc_t.B
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic 0.000
## race_ethnicity.blBlack 0.001 0.355
## race_ethnicity.blOther 0.000 0.287 0.261
## high.educ.blBachelor -0.004 -0.022 -0.017
## high.educ.blSome College 0.000 -0.112 -0.086
## high.educ.blHS Diploma/GED 0.002 -0.144 -0.148
## high.educ.bl< HS Diploma -0.005 -0.169 -0.078
## prnt.empl.blStay at Home Parent 0.002 0.043 0.092
## prnt.empl.blUnemployed -0.002 0.010 -0.041
## prnt.empl.blOther 0.002 0.040 0.011
## neighb_phenx_avg_p.bl.cm -0.004 0.029 0.137
## overall.income.bl[>=50K & <100K] -0.006 -0.087 -0.060
## overall.income.bl[<50k] -0.004 -0.141 -0.180
## overall.income.bl[Don't Know or Refuse] -0.008 -0.095 -0.123
## sex.blFemale 0.002 -0.008 -0.018
## reshist_addr1_pm252016aa_bl.c5 -0.006 -0.084 -0.031
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.923 0.003 0.003
## rc_t.O hgh..B hg..SC
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor -0.003
## high.educ.blSome College -0.026 0.461
## high.educ.blHS Diploma/GED -0.010 0.339 0.502
## high.educ.bl< HS Diploma -0.013 0.268 0.413
## prnt.empl.blStay at Home Parent 0.018 -0.031 -0.016
## prnt.empl.blUnemployed 0.011 -0.010 -0.011
## prnt.empl.blOther -0.011 -0.014 -0.033
## neighb_phenx_avg_p.bl.cm 0.041 -0.005 0.061
## overall.income.bl[>=50K & <100K] -0.010 -0.176 -0.276
## overall.income.bl[<50k] -0.079 -0.161 -0.417
## overall.income.bl[Don't Know or Refuse] -0.055 -0.101 -0.253
## sex.blFemale -0.019 0.014 0.021
## reshist_addr1_pm252016aa_bl.c5 -0.026 0.001 -0.017
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.002 0.004 0.001
## h..HSD h..<HD p..aHP
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma 0.381
## prnt.empl.blStay at Home Parent -0.050 -0.095
## prnt.empl.blUnemployed -0.069 -0.099 0.149
## prnt.empl.blOther -0.014 -0.020 0.159
## neighb_phenx_avg_p.bl.cm 0.056 0.049 0.028
## overall.income.bl[>=50K & <100K] -0.174 -0.116 -0.028
## overall.income.bl[<50k] -0.367 -0.311 -0.052
## overall.income.bl[Don't Know or Refuse] -0.240 -0.220 -0.075
## sex.blFemale 0.014 -0.005 -0.006
## reshist_addr1_pm252016aa_bl.c5 -0.009 -0.017 -0.018
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.001 0.006 0.001
## prn..U prn..O n___..
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther 0.134
## neighb_phenx_avg_p.bl.cm 0.022 0.004
## overall.income.bl[>=50K & <100K] -0.014 -0.048 0.080
## overall.income.bl[<50k] -0.100 -0.138 0.151
## overall.income.bl[Don't Know or Refuse] -0.077 -0.097 0.083
## sex.blFemale 0.019 0.017 0.027
## reshist_addr1_pm252016aa_bl.c5 -0.002 -0.001 0.062
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.003 -0.001 0.003
## o..[&< o..[<5 o..KoR
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k] 0.508
## overall.income.bl[Don't Know or Refuse] 0.363 0.488
## sex.blFemale -0.005 -0.006 0.009
## reshist_addr1_pm252016aa_bl.c5 -0.018 -0.031 -0.031
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.006 0.004 0.008
## sx.blF r_1_25
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k]
## overall.income.bl[Don't Know or Refuse]
## sex.blFemale
## reshist_addr1_pm252016aa_bl.c5 -0.004
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.000 0.006
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -3.4078102 -0.7131608 -0.3394560 0.4062731 3.9975825
##
## Number of Observations: 23857
## Number of Groups:
## abcd_site subjectid %in% abcd_site
## 21 9345summary(aggressive_zinb_r$zi.fit)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## NA NA NA
##
## Random effects:
## Formula: ~1 | abcd_site
## (Intercept) Residual
## StdDev: 0.2859857 0.4689479
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: zp ~ reshist_addr1_no2_2016_aavg_bl.c533 * interview_age.c9.y + race_ethnicity.bl + high.educ.bl + prnt.empl.bl + neighb_phenx_avg_p.bl.cm + overall.income.bl + sex.bl + reshist_addr1_pm252016aa_bl.c5
## Value Std.Error
## (Intercept) -4.464757 0.13898278
## reshist_addr1_no2_2016_aavg_bl.c533 -0.003421 0.00834420
## interview_age.c9.y 0.261613 0.04528161
## race_ethnicity.blHispanic 0.348753 0.05979107
## race_ethnicity.blBlack 0.725354 0.06155347
## race_ethnicity.blOther 0.467685 0.05663682
## high.educ.blBachelor 0.136341 0.04985510
## high.educ.blSome College 0.234711 0.05831398
## high.educ.blHS Diploma/GED 0.379336 0.08018152
## high.educ.bl< HS Diploma 1.010817 0.08734638
## prnt.empl.blStay at Home Parent -0.028632 0.05173440
## prnt.empl.blUnemployed 0.228653 0.07369051
## prnt.empl.blOther -0.164570 0.07951712
## neighb_phenx_avg_p.bl.cm 0.263897 0.02232451
## overall.income.bl[>=50K & <100K] -0.297203 0.05201787
## overall.income.bl[<50k] -0.532671 0.06533646
## overall.income.bl[Don't Know or Refuse] 0.043443 0.07168271
## sex.blFemale 0.200690 0.03650075
## reshist_addr1_pm252016aa_bl.c5 0.069745 0.01932826
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.001164 0.00319674
## DF t-value p-value
## (Intercept) 23817 -32.12453 0.0000
## reshist_addr1_no2_2016_aavg_bl.c533 23817 -0.41000 0.6818
## interview_age.c9.y 23817 5.77746 0.0000
## race_ethnicity.blHispanic 23817 5.83286 0.0000
## race_ethnicity.blBlack 23817 11.78414 0.0000
## race_ethnicity.blOther 23817 8.25762 0.0000
## high.educ.blBachelor 23817 2.73475 0.0062
## high.educ.blSome College 23817 4.02495 0.0001
## high.educ.blHS Diploma/GED 23817 4.73096 0.0000
## high.educ.bl< HS Diploma 23817 11.57251 0.0000
## prnt.empl.blStay at Home Parent 23817 -0.55343 0.5800
## prnt.empl.blUnemployed 23817 3.10288 0.0019
## prnt.empl.blOther 23817 -2.06961 0.0385
## neighb_phenx_avg_p.bl.cm 23817 11.82096 0.0000
## overall.income.bl[>=50K & <100K] 23817 -5.71347 0.0000
## overall.income.bl[<50k] 23817 -8.15274 0.0000
## overall.income.bl[Don't Know or Refuse] 23817 0.60604 0.5445
## sex.blFemale 23817 5.49823 0.0000
## reshist_addr1_pm252016aa_bl.c5 23817 3.60847 0.0003
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 23817 -0.36399 0.7159
## Correlation:
## (Intr) rs_1_2_2016__.533
## reshist_addr1_no2_2016_aavg_bl.c533 -0.701
## interview_age.c9.y -0.659 0.721
## race_ethnicity.blHispanic -0.041 -0.044
## race_ethnicity.blBlack -0.031 -0.067
## race_ethnicity.blOther -0.088 -0.024
## high.educ.blBachelor -0.135 -0.002
## high.educ.blSome College -0.096 0.008
## high.educ.blHS Diploma/GED -0.065 0.000
## high.educ.bl< HS Diploma -0.019 -0.032
## prnt.empl.blStay at Home Parent -0.065 -0.001
## prnt.empl.blUnemployed -0.024 -0.010
## prnt.empl.blOther -0.026 0.000
## neighb_phenx_avg_p.bl.cm -0.150 0.065
## overall.income.bl[>=50K & <100K] -0.058 -0.022
## overall.income.bl[<50k] -0.021 -0.026
## overall.income.bl[Don't Know or Refuse] -0.018 -0.024
## sex.blFemale -0.145 0.005
## reshist_addr1_pm252016aa_bl.c5 -0.225 -0.175
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.600 -0.785
## in_.9. rc_t.H rc_t.B
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic 0.002
## race_ethnicity.blBlack 0.011 0.409
## race_ethnicity.blOther 0.007 0.341 0.309
## high.educ.blBachelor -0.014 -0.005 -0.003
## high.educ.blSome College -0.003 -0.114 -0.093
## high.educ.blHS Diploma/GED 0.009 -0.142 -0.145
## high.educ.bl< HS Diploma -0.016 -0.169 -0.090
## prnt.empl.blStay at Home Parent -0.002 0.048 0.101
## prnt.empl.blUnemployed -0.001 0.018 -0.027
## prnt.empl.blOther 0.010 0.043 0.002
## neighb_phenx_avg_p.bl.cm -0.001 0.016 0.122
## overall.income.bl[>=50K & <100K] -0.013 -0.098 -0.087
## overall.income.bl[<50k] -0.011 -0.133 -0.200
## overall.income.bl[Don't Know or Refuse] -0.021 -0.107 -0.144
## sex.blFemale 0.009 -0.004 -0.024
## reshist_addr1_pm252016aa_bl.c5 -0.015 -0.059 -0.024
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.917 0.005 0.003
## rc_t.O hgh..B hg..SC
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor 0.018
## high.educ.blSome College -0.005 0.452
## high.educ.blHS Diploma/GED 0.005 0.339 0.507
## high.educ.bl< HS Diploma 0.002 0.317 0.491
## prnt.empl.blStay at Home Parent 0.020 -0.029 -0.017
## prnt.empl.blUnemployed 0.014 -0.015 -0.009
## prnt.empl.blOther -0.019 -0.020 -0.029
## neighb_phenx_avg_p.bl.cm 0.027 0.002 0.055
## overall.income.bl[>=50K & <100K] -0.022 -0.154 -0.292
## overall.income.bl[<50k] -0.075 -0.138 -0.397
## overall.income.bl[Don't Know or Refuse] -0.061 -0.105 -0.291
## sex.blFemale -0.012 0.012 0.017
## reshist_addr1_pm252016aa_bl.c5 -0.007 0.005 -0.010
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.005 0.013 0.006
## h..HSD h..<HD p..aHP
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma 0.476
## prnt.empl.blStay at Home Parent -0.051 -0.113
## prnt.empl.blUnemployed -0.080 -0.130 0.176
## prnt.empl.blOther -0.012 -0.030 0.144
## neighb_phenx_avg_p.bl.cm 0.047 0.061 0.029
## overall.income.bl[>=50K & <100K] -0.188 -0.148 -0.022
## overall.income.bl[<50k] -0.379 -0.387 -0.047
## overall.income.bl[Don't Know or Refuse] -0.293 -0.312 -0.090
## sex.blFemale 0.011 -0.004 0.004
## reshist_addr1_pm252016aa_bl.c5 0.004 -0.022 -0.005
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.000 0.025 0.011
## prn..U prn..O n___..
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther 0.140
## neighb_phenx_avg_p.bl.cm 0.033 -0.008
## overall.income.bl[>=50K & <100K] -0.017 -0.046 0.073
## overall.income.bl[<50k] -0.097 -0.124 0.130
## overall.income.bl[Don't Know or Refuse] -0.099 -0.112 0.089
## sex.blFemale 0.036 0.013 0.023
## reshist_addr1_pm252016aa_bl.c5 0.003 0.006 0.048
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.006 -0.006 0.002
## o..[&< o..[<5 o..KoR
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k] 0.475
## overall.income.bl[Don't Know or Refuse] 0.392 0.556
## sex.blFemale 0.001 0.000 0.001
## reshist_addr1_pm252016aa_bl.c5 -0.012 -0.020 -0.036
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.014 0.011 0.029
## sx.blF r_1_25
## reshist_addr1_no2_2016_aavg_bl.c533
## interview_age.c9.y
## race_ethnicity.blHispanic
## race_ethnicity.blBlack
## race_ethnicity.blOther
## high.educ.blBachelor
## high.educ.blSome College
## high.educ.blHS Diploma/GED
## high.educ.bl< HS Diploma
## prnt.empl.blStay at Home Parent
## prnt.empl.blUnemployed
## prnt.empl.blOther
## neighb_phenx_avg_p.bl.cm
## overall.income.bl[>=50K & <100K]
## overall.income.bl[<50k]
## overall.income.bl[Don't Know or Refuse]
## sex.blFemale
## reshist_addr1_pm252016aa_bl.c5 -0.006
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y -0.003 0.016
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.2606977 -0.3647646 -0.2733282 0.2186763 19.2682499
##
## Number of Observations: 23857
## Number of Groups: 21anova(aggressive_zinb_r)## numDF denDF F-value
## (Intercept) 1 14510 342.5889
## reshist_addr1_no2_2016_aavg_bl.c533 1 9307 0.0336
## interview_age.c9.y 1 14510 81.7451
## race_ethnicity.bl 3 9307 2.5127
## high.educ.bl 4 9307 21.8311
## prnt.empl.bl 3 9307 13.1074
## neighb_phenx_avg_p.bl.cm 1 9307 82.3797
## overall.income.bl 3 9307 9.9956
## sex.bl 1 9307 88.8603
## reshist_addr1_pm252016aa_bl.c5 1 9307 1.6615
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 1 14510 6.9335
## p-value
## (Intercept) <.0001
## reshist_addr1_no2_2016_aavg_bl.c533 0.8546
## interview_age.c9.y <.0001
## race_ethnicity.bl 0.0567
## high.educ.bl <.0001
## prnt.empl.bl <.0001
## neighb_phenx_avg_p.bl.cm <.0001
## overall.income.bl <.0001
## sex.bl <.0001
## reshist_addr1_pm252016aa_bl.c5 0.1974
## reshist_addr1_no2_2016_aavg_bl.c533:interview_age.c9.y 0.0085Assumption checking for ZINB Models
#Check outlier/residuals with this df
aggressive_res <- df_cc
aggressive_res$level1_resid.raw <- residuals(aggressive_zinb_r)
aggressive_res$level1_resid.pearson <- residuals(aggressive_zinb_r, type="pearson")
#Add predicted values (Yhat)
aggressive_res$cbcl_scr_syn_aggressive_r_predicted <- predict(aggressive_zinb_r,aggressive_res,type="response")
#Incidence
aggressive_res$incidence <- estimate.probability(aggressive_res$cbcl_scr_syn_aggressive_r, method="empirical")
#Plotting histogram of residuals, but may be skewed since using ZINB, so make sure to check below plots
hist(aggressive_res$level1_resid.pearson)
### Incidence vs. X’s Plots
#age
ggplot(aggressive_res,aes(incidence,interview_age)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
#pm2.5
ggplot(aggressive_res,aes(incidence,reshist_addr1_no2_2016_aavg_bl)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
### Residuals vs Y (CBCL Outcome) Plot
plot(aggressive_res$level1_resid.pearson, aggressive_res$cbcl_scr_syn_aggressive_r)
### Residuals vs Yhat Plot
plot(aggressive_res$level1_resid.pearson, aggressive_res$cbcl_scr_syn_aggressive_r_predicted)
### Residuals vs Row Plot
plot(as.numeric(rownames(aggressive_res)),aggressive_res$level1_resid.pearson)
### Residuals vs X’s Plots
#age
ggplot(aggressive_res,aes(level1_resid.pearson,interview_age)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
#pm2.5
ggplot(aggressive_res,aes(level1_resid.pearson,reshist_addr1_no2_2016_aavg_bl)) + geom_point(color = "black") + geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'